Outcomes Management

I think the best method for improving patient outcomes is the Nursing Process. It is a requirement of every nurse practice act i know of.

In my state it is outlined here:

http://www.rn.ca.gov/pdfs/regulations/npr-i-20.pdf

The nursing process is also required for all acute care hospitals:

https://govt.westlaw.com/calregs/Document/IFD69DB90621311E2998CBB33624929B8?viewType=FullText&originationContext=documenttoc&transitionType=CategoryPageItem&contextData=%28sc.Default%29

I think a similar scientific problem solving process works well to improve patient outcomes on the unit and facility wide levels.

I once served on my hospitals Professional Practice Committee. We were able to improve staffing, enforce competency validation and orientation to the specific unit before being assigned to one or more patients, and to ensure sufficient appropriate equipment and supplies.

Unsafe staffing is the most common barrier to the best possible patient outcomes.

http://www.nursing.upenn.edu/chopr/Documents/Aiken.2010.CaliforniaStaffingRatios.pdf

There used to be an Outcomes Management" journal.

http://www.nursingcenter.com/lnc/journalissue?Journal_ID=54038&Issue_ID=428542

Because people are hospitalized for the purpose of nursing care in my state the registered nurse is responsible for collaboration with other disciplines. The nursing plan for the patient's care must be discussed with and developed as a result of coordination with the patient, the patient's family, or other representatives, when appropriate, and staff of other disciplines involved in the care of the patient.

May I suggest if you don't want to answer a query, just skip over topic. AN members are welcome to post about topics, especially when in nursing program.

In my experience, the IT design of EMR database is critical in being able to abstract patient and clinical data.

Quality of Care and the Outcomes Management Movement

...Process measures are frequently used in performance measurement. Process measures are generally much easier to construct, require less data collection and analysis to produce, and are easier for both clinicians and non-clinicians to understand. Many performance measurement systems, such as the Health Plan Employer Data Information Set (HEDIS), are primarily measures of process of care. ⁵ Process improvement, when linked to processes proven by randomized clinical trials to improve outcomes, is an important part of continuous quality improvement (CQI). Implementation of CQI programs based on process improvement can reduce variation and enhance patient care. ¹⁶ An example of process improvement is the present effort by many EDs across the country to maximize the percentage of AMI patients receiving thrombolytic agents and to reduce the time to administration of these agents.

Although the development of process measures is generally easier than the development of outcome measures, certain steps must be followed to ensure the clinical pertinence and precision of each process measure. Important steps in the development of process measures include identification of the process of interest, review of the evidence supporting the process, development of a process indicator (including eligibility in the numerator and denominator), development of a standardized data collection system, and generation of the process indicator. ¹⁶

When developing process measures for a specific setting, such as an emergency department, several factors need to be considered. Such factors include what populations are cared for and are of interest, what process measures external agencies are examining, what process measures are likely to reveal opportunity for improvement, and the strength of the evidence linking the process and the desired outcome.

When using process indicators to measure health care delivery, the strength of the association between the process indicator and the outcome of interest must be examined.¹⁷ A grading system has been developed that evaluates the strength of practice guidelines used in process measure development. ¹⁸ In this system, an "A" is given to those guidelines, or process measures, that have support from large, well-controlled, randomized clinical trials. Examples of this type of guideline include reperfusion in eligible AMI patients and ACE inhibitors in heart failure patients with systolic dysfunction. ^{19, 20} A "B" rating is given to guidelines, or process measures, that have support from observational studies or small randomized clinical trials (e.g. the use of angioplasty versus thrombolytic agents in AMI patients). ²¹ Finally, a "C" rating denotes those guidelines or process measures that are developed from expert opinion but which have little scientific evidence to support the process indicators (e.g. the Agency for Healthcare Policy and Research's low back pain guidelines, most of which is supported by expert opinion). ²² Choosing processes strongly linked to a favorable outcome removes the need to measure the outcome.

Development of the process indicator requires the definition of eligible populations, development of an abstraction tool and methods of data collection, and standardization of data collection using a data dictionary. These steps are time-consuming and costly, but are essential to ensure the reliability and validity of the process indicator. When these steps have been executed appropriately the generation of the process indicator requires minimal analysis.

Outcome measures

Clinical outcome measures examine discrete, patient-focused endpoints such as readmission, length of stay, morbidity and mortality. When using outcomes for measuring the performance of health care delivery systems it is often necessary to develop an adjustment system that isolates the contribution of the health care system to the outcome.²³ The importance of this principle is exemplified in the association of cancer mortality with age. Cancer mortality increases dramatically with age. If we were to compare two health care systems regarding their cancer death rates and not account for the fact that the mean age of one health care system's population was 20 years greater than the other, we would come to erroneous conclusions. What is required in this situation is risk adjustment — a method of adjusting the outcome, in this case cancer mortality, for the underlying cancer risk in the population due to age. Risk-adjustment can remove the effect of the confounder, age, from the outcome of interest, cancer. The necessity of risk-adjustment to level the playing field becomes obvious when payers and consumers are using the outcome measures to make purchasing decisions. The New York Department of Health's outcome measurement model for coronary bypass surgery is an example of a risk adjustment measure.²⁴ This model removed the contribution of the patient's severity of disease, demographics, and comorbid conditions, to the outcome, mortality. After removing these patient factors affecting the procedure's survival rate, the remaining data showed the contribution of the health care providers to survival.

Concerns regarding factors such as data sources, adjustment models, and attributable risk have been raised with these measurements. ²³ These concerns are well-founded and must be addressed. Outcome measurement systems that are not developed with scientific rigor can lead to erroneous conclusions for consumers, payers, and health care providers....

Merged threads on same topic