QUALITY MANAGERS' ROLE IN OUTCOMES MANAGEMENT

By: Patrice L. Spath
Brown-Spath & Associates

Studying the relationship between health care processes and patient outcomes is not a new science however it has assumed greater importance in today's economic climate. Medical effectiveness and outcomes management studies, once the purview of health services researchers, are moving to the individual provider setting. This evolution has tremendous impact on quality management (QM) professionals. As the guardians of the data which is indispensable for medical effectiveness and outcomes management investigations, QM professionals play a key role in helping caregivers collect and analyze process and outcome measures.

Data are the prominent ingredient in each step of an outcomes management project. Clinicians must have valid and reliable information to enable them to make wise choices about the ideal process, to evaluate the stability of the process, and enhance improvements. QM professionals should partner with clinicians to ensure the right information is being used for outcomes management initiatives. For some QM professionals this may require the acquisition of new knowledge and skills.

Familiarize Yourself with Outcome Measurement Tools

Learn more about patient-reported outcomes measurement tools and how they are used to capture data about functional and quality of life issues. Many organizations have developed patient reporting mechanisms to measure health care performance and their projects are regularly written up in professional journals. For example, the Center for Quality of Care Research and Education of the Harvard School of Public Health has developed a self-administered patient questionnaire that asks for data concerning the time to receive services (access to care), communication between providers (coordination of care), and follow-up after tests and treatment (continuity of care). The tool, entitled PROSPER (Patient Reports on System Performance) can be used by physicians and health plan managers to compare performance among ambulatory care centers and to track performance over time.(1)

Standardize Coding Practices

Be sure that diagnosis and procedure coding practices are being applied equally to all patient groups. Complete and accurate coding of all diagnoses and procedures for Medicare patient records has become important. However, the focus on correct coding for Medicare patients could easily cause coders to spend less time capturing secondary diagnoses for non-Medicare patients just to expedite the billing process. Billing-driven coding practices must be replaced by accuracy-driven incentives. Data about all types of patients, regardless of payer source, are used for outcomes management purposes. Accurate coding of all diagnoses and procedures for every patient who comes in contact with the health care delivery system is imperative. In addition, qualified and trained coders must be employed in all health care settings to ensure reliable information is contained in inpatient and outpatient databases. For more information about coder training and credentials visit the web site of the American Health Information Management Association (http://www.ahima.org).

Improve Data Displays

An evaluation of the inter-relationship of processes and outcomes requires more than accurate data. This data must be transformed into information that is comprehensive to the users. To this accomplish this goal, information must report variation in events of interest and facilitate a comparison with outcomes generated from other institutions. For outcomes management purposes, the information must also illustrate the relationship between the treatment of a illness and the immediate, intermediate and/or long-term outcomes achieved following the treatment.

Analyses of the relationship between the process of care and the outcomes of care go beyond simple calculations of rate-based measures. Caregivers select those processes which they feel have the greatest impact on outcomes. Data about these important processes are collected and correlated with the outcome measure. This technique for displaying information is termed stratification. Stratification involves the breakdown of information into meaningful categories or classifications to focus analysis on those patient care activities that appear to yield less than desirable outcomes. Stratification of data provides an opportunity to demonstrate a relationship where none was thought to exist or to discredit a relationship that was presumed to exist. As a secondary benefit, stratification of data helps the users uncover the probable cause of a less than desirable outcome.

Another graphical technique used to analyze the relationship between two variables is a scatter diagram. Two sets of data are plotted on a graph, with the y axis being used for the variable to be predicted and the x axis being used for the variable to make the prediction. Scatter diagrams are useful for:

- Studying the possible relationship between one variable and another (unlike
   other graphs, only two processes or two variables within a process are
   compared)
- Displaying what happens to one variable when another variable changes
- Showing the interrelationship of causes
- Creating new performance measures after a the ideal process definition has been
   revised

The book, "Outcomes Management: Using Data for Decision Making," teaches QM professionals how to effectively use statistical tools and data displays in outcomes management projects. To learn more about this book visit the product page of the Brown-Spath & Associates' Internet site (http://www.brownspath.com)

Information Challenges Continue

Today's emphasis on outcomes management will likely continue, as many clinicians see this approach to research as being key to improved cost efficiency and quality in the health care delivery system. QM professionals should be involved in all phases of outcomes management projects. Their advice, support, and expertise is crucial to the success of the project.

Reference:
(1) Hargraves, J.L., et al. "Using patient reports to measure health care system performance." Clinical Performance and Quality Health Care. 1, no. 4 (1993):208-213.