QIPMO

The Quality Improvement Program for Missouri's Long-Term Care Facilities (QIPMO) will send gerontologic nurse specialists to Missouri nursing facilities. They will provide information and assistance related to clinical issues of interest to long-term care staff. QIPMO is a cooperative program between the MU Sinclair School of Nursing and the Missouri Department of Health and Senior Services (DHSS) with funding by DHSS using the Missouri Quality of Care fund (senate bill 556). QIPMO nurses are not surveyors from the Department of Health and Senior Services. The site visits are treated confidential.

If your facility is in Missouri, QIPMO nurse consultants will help refine your quality improvment programs. Members of the QIPMO team can also help you learn to download, read, and interpret your state and federal MDS quarterly reports and help simplify the quarterly MDS data. The nurses can also provide clinical practice consultations and
inservice training sessions for your staff to help improve nursing care in those areas where you would like to see improvement.

In the area below you will find a list of recently added QIPMO research articles.

The purpose of this study was to describe the processes of care, organizational attributes, cost of care, staffing level, and staff mix in a sample of Missouri homes with good, average, and poor resident outcomes. In facilities with good resident outcomes, there are basics of care and processes surrounding each that staff consistently do: helping residents with ambulation, nutrition and hydration, and toileting and bowel regularity; preventing skin breakdown; and managing pain. For nursing homes to achieve good resident outcomes, they must have leadership that is willing to embrace quality improvement and group process and see that the basics of care delivery are done for residents. Good quality care may not cost more than poor quality care; there is some evidence that good quality care may cost less. Small facilities of 60 beds were more likely to have good resident outcomes. Strategies have to be considered so larger facilities can be organized into smaller clusters of units that could function as small nursing homes within the larger whole.

Rantz, M.J., Hicks, L., Grando, V.T., Petroski, G.F., Madsen, R.W., Mehr, D.R., Conn, V., Zwygart-Stauffacher, M., Scott, J., Flesner, M., Bostick, J., Porter, R., & Maas, M. (2004). Nursing home quality, cost, staffing, and staff-mix. The Gerontologist, 44(1): 24-38.

In 1994 12.7% of the population was 65 and over, while 10.6% were 85 and over. Expenditures for nursing homes reached $72.3 billion in 1994 (much of which is tax-supported) accounting for 8.7% of all personal health money spent. Data from the 1993 Missouri Medicaid cost reports for 403 nursing homes were reviewed to determine differences in costs per resident day (PRD) and discover which factors most influenced these differences. Mid-sized facilities with 60-120 beds reported the lowest resident-related PRD costs. PRD expenses for aides and orderlies were higher in tax-exempt facilities, which was thought to be related to their "more altruistic" mission. Investor-owned facilities showed significantly greater administrative costs PRD, which may relate to higher administrative salaries and fancier offices. The authors suggest further study that would incorporate location, occupancy rate, quality of care, case mix, and payer mix data.

Hicks, L.L., Rantz, M. J., Petroski, G.F., Madsen, R.W., Conn, V.S., Mehr, D., & Porter, R. (1997). Assessing contributors to cost of care in nursing homes. Nursing Economics, 15(4), 205-212.

An important area of inquiry in quality measurement when using quality indicators (QIs) lies in determining what thresholds indicate good and poor resident outcomes. In July 1996, a cross-section of 13 clinical care personnel from nursing homes participated on an expert panel for threshold setting of Qls derived from Minimum Data Set (MDS) assessment data. Panel members met as a group for a day, individually determined good and poor threshold scores for each QI, reviewed statewide distributions of MDS Qls, and completed a follow-up Delphi round of the final results. Reports of MDS scores that are sent to a group of nursing homes in Missouri now include thresholds established for good and poor scores so the facilities can easily see where they are performing well and where they need to concentrate quality improvement efforts. This article describes the efforts made to develop and disseminate the thresholds for MDS scores.

Rantz, M. J., Petroski, G.F., Madsen, R.W., Scott, J., Mehr, D., Popejoy, L., Hicks, L., Porter, R., Zwygart-Stauffacher, M., & Grando, V. (1997). Setting thresholds for MDS quality indicators for nursing home quality improvement reports. Joint Commission Journal on Quality Improvement, 23(11), 602-611.

Determining meaningful thresholds to reinforce excellent performance and flag potential problem areas in nursing home care is critical for preparing reports for nursing homes to use in their quality improvement programs. This article builds on the work of an earlier panel of experts that set thresholds for quality indicators (QIs) derived from Minimum Data Set (MDS) assessment data. Thresholds were now set for the revised MDS 2.0 two-page quarterly form and Resource Utilization Groups III (RUGS III) quarterly instrument.

Rantz, M. J., Petroski, G., Madsen, R., Mehr, D., Popejoy, L., Hicks, L., Porter, R., Zwygart-Stauffacher, M. & Grando, V. (2000). Setting thresholds for quality indicators derived from MDS data for nursing home quality improvement reports: An update. Joint Commission Journal on Quality Improvement, 26(2), 101-110.

Over the past 12 years, members of the Minimum Data Set (MDS) and Quality Research Team at the University of Missouri-Columbia have been working with the Missouri Department of Health and Senior Services to improve care in Missouri nursing homes. The team conducted initial qualitative studies that explored the multidimensional aspects of quality of nursing home care. Using grounded theory methods, dimensions important to consumers and overlapping dimensions important to providers, healthcare professionals, and regulators were identified. The quality dimensions were operationalized in a theoretical model of quality of nursing home care. We learned from the studies that quality of care in nursing homes is a concept that encompasses broad dimensions of not only technical care provided but also the context or environment in which the care is delivered.

Rantz, M.J., Zwygart-Stauffacher, M., & Flesner, M. (2005). Advances in measuring quality of care in nursing homes: a new tool for providers, consumers, regulators, and researchers. Journal of Nursing Care Quality, 20(4), 293-296.

AgingMO is Copyrighted by the Sinclair School of Nursing

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