Mary Zwygart-Stauffacher

Nursing home care quality: a multidimensional theoretical model integrating the views of consumers and providers.

This exploratory study was undertaken to discover the defining dimensions of nursing home care quality from the viewpoint of consumers of nursing home care. Eleven focus groups were conducted in five Missouri communities. The seven dimensions of the consumer multidimensional model of nursing home care quality are: staff, care, family involvement, communication, environment, home, and cost. The views of consumers and families are compared with the results of a previous study of providers of nursing home services. An integrated, multidimensional theoretical model is presented for testing and evaluation. An instrument based on the model is being tested to observe and score the dimensions of nursing home care quality.

Nursing Home Care Quality: A Multidimensional Theoretical Model

This exploratory study was undertaken to discover the defining dimensions of nursing home care quality and to propose a conceptual model to guide nursing home quality research and the development of instruments to measure nursing home care quality. Three focus groups were conducted in three central Missouri communities. A naturalistic inductive analysis of the transcribed content was completed. Two core variables (interaction and odor) and several related concepts emerged from the data. Using the core variables, related concepts, and detailed descriptions from participants, three models of nursing home care quality emerged from the analysis: (1) a model of a nursing home with good quality care; (2) a model of a nursing home with poor quality care; and (3) a multidimensional model of nursing home care quality. The seven dimensions of the multidimensional model of nursing home care quality are: central focus, interaction, milieu, environment, individualized care, staff, and safety. To pursue quality, the many dimensions must be of primary concern to nursing homes. We are testing an instrument based on the model to observe and score the dimensions of nursing home care quality.

Verifying Nursing Home Care Quality Using Minimum Data Set Quality Indicators and Other Quality Measures

Researchers, providers and government agencies have devoted time and resources to the development of a set of Quality Indicators derived from Minimum Data Set (MDS) data. Little effort has been directed toward verifying that Quality Indicators derived from MDS data accurately measure nursing home quality. Researchers at the University of Missouri-Columbia have independently verified the accuracy of QI derived from MDS data using four different methods; 1) structured participative observation, 2) QI Observation Scoring Instrument, 3) Independent Observable Indicators of Quality Instrument, and 4) survey citations. Our team was able to determine that QIs derived from MDS data did differentiate nursing homes of good quality from those of poorer quality.

Setting thresholds for quality indicators derived from MDS data for nursing home quality improvement reports: an update.

BACKGROUND 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. SETTING THRESHOLDS In a day-long session in October 1998, panel members individually determined lower (good) and upper (poor) threshold scores for each QI, reviewed statewide distributions of MDS QIs, and completed a follow-up Delphi of the final results. REPORTING MDS QIS FOR QUALITY IMPROVEMENT The QI reports compiled longitudinal data for all residents in the nursing home during each quarter and cumulatively displayed data for five quarters for each QI. A resident roster was provided to the nursing home so that the quality improvement team could identify the specific residents who developed the problems defined by each QI during the last quarter. Quality improvement teams found the reports helpful and easy to interpret. SUMMARY AND CONCLUSIONS As promised in an earlier report, to ensure that thresholds reflect current practice, research using experts in a panel to set thresholds was repeated as needed. As the MDS instrument or recommended calculations for the MDS QIs change, thresholds will be reestablished to ensure a fit with the instrument and data.

Minimum Data Set and Resident Assessment Instrument. Can using standardized assessment improve clinical practice and outcomes of care

Regulating and standardizing the assessment of residents was envisioned by the 1986 Committee on Nursing Home Reform to have many advantages for facility management, government regulatory agencies, and clinical staff to evaluate changes in resident status and adjust the care plans accordingly. Standardized assessment data was viewed as a source of management information to be used to track case mix (i.e., acuity) of residents, allocate resources such as staff, and evaluate care quality. The Resident Assessment Instrument is a clinically relevant assessment process that can facilitate effective care planning, interventions, and quality improvement. It is a clinically complex process requiring care delivery systems developed by RNs to support the implementation of individualized care.

Cost, staffing and quality impact of bedside electronic medical record (EMR) in nursing homes.

OBJECTIVE There is growing political pressure for nursing homes to implement the electronic medical record (EMR) but there is little evidence of its impact on resident care. The purpose of this study was to test the unique and combined contributions of EMR at the bedside and on-site clinical consultation by gerontological expert nurses on cost, staffing, and quality of care in nursing homes. METHODS Eighteen nursing facilities in 3 states participated in a 4-group 24-month comparison: Group 1 implemented bedside EMR, used nurse consultation; Group 2 implemented bedside EMR only; Group 3 used nurse consultation only; Group 4 neither. Intervention sites (Groups 1 and 2) received substantial, partial financial support from CMS to implement EMR. Costs and staffing were measured from Medicaid cost reports, and staff retention from primary data collection; resident outcomes were measured by MDS-based quality indicators and quality measures. RESULTS Total costs increased in both intervention groups that implemented technology; staffing and staff retention remained constant. Improvement trends were detected in resident outcomes of ADLs, range of motion, and high-risk pressure sores for both intervention groups but not in comparison groups. DISCUSSION Implementation of bedside EMR is not cost neutral. There were increased total costs for all intervention facilities. These costs were not a result of increased direct care staffing or increased staff turnover. CONCLUSIONS Nursing home leaders and policy makers need to be aware of on-going hardware and software costs as well as costs of continual technical support for the EMR and constant staff orientation to use the system. EMR can contribute to the quality of nursing home care and can be enhanced by on-site consultation by nurses with graduate education in nursing and expertise in gerontology.

Randomized Multilevel Intervention to Improve Outcomes of Residents in Nursing Homes in Need of Improvement

OBJECTIVES A comprehensive multilevel intervention was tested to build organizational capacity to create and sustain improvement in quality of care and subsequently improve resident outcomes in nursing homes in need of improvement. DESIGN/SETTING/PARTICIPANTS Intervention facilities (N = 29) received a 2-year multilevel intervention with monthly on-site consultation from expert nurses with graduate education in gerontological nursing. Attention control facilities (N = 29) that also needed to improve resident outcomes received monthly information about aging and physical assessment of elders. INTERVENTION The authors conducted a randomized clinical trial of nursing homes in need of improving resident outcomes of bladder and bowel incontinence, weight loss, pressure ulcers, and decline in activities of daily living. It was hypothesized that following the intervention, experimental facilities would have higher quality of care, better resident outcomes, more organizational attributes of improved working conditions than control facilities, higher staff retention, similar staffing and staff mix, and lower total and direct care costs. RESULTS The intervention did improve quality of care (P = .02); there were improvements in pressure ulcers (P = .05) and weight loss (P = .05). Organizational working conditions, staff retention, staffing, and staff mix and most costs were not affected by the intervention. Leadership turnover was surprisingly excessive in both intervention and control groups. CONCLUSION AND IMPLICATIONS Some facilities that are in need of improving quality of care and resident outcomes are able to build the organizational capacity to improve while not increasing staffing or costs of care. Improvement requires continuous supportive consultation and leadership willing to involve staff and work together to build the systematic improvements in care delivery needed. Medical directors in collaborative practice with advanced practice nurses are ideally positioned to implement this low-cost, effective intervention nationwide.

Field testing, refinement, and psychometric evaluation of a new measure of nursing home care quality.

The primary aim of this NINR-NIH–funded field test in 407 nursing homes in 3 states was to complete the development of and conduct psychometric testing for the Observable Indicators of Nursing Home Care Quality Instrument (Observable Indicators, OIQ). The development of the OIQ was based on extensive qualitative and iterative quantitative work that described nursing home care quality and did initial validity and reliability field testing of the instrument in 123 nursing homes in 1 state. The scale is meant for researchers, consumers, and regulators interested in directly observing and quickly evaluating (within 30 minutes of observation) the multiple dimensions of care quality in nursing homes. After extensive testing in this study, the Observable Indicators instrument has been reduced to 30 reliable and discriminating items that have a conceptually coherent hierarchical factor structure that describes nursing home care quality. Seven first-order factors group together into two second-order factors of Structure (includes Environment: Basics and Odors) and Process (includes Care Delivery, Grooming, Interpersonal Communication, Environment: Access, and Environment: Homelike) that are classic constructs of Quality, which was the third-order factor. Internal consistency reliability for the 7 first-order factors ranged from .77 to .93. Construct validity analyses revealed an association between survey citations and every subscale as well as the total score of the OIQ instrument. Known groups analysis revealed expected trends in the OIQ scores. The Observable Indicators instrument as a whole shows acceptable interrater and test-retest reliabilities, and strong internal consistency. Scale subscales show acceptable reliability as well. Generalizability Theory analyses revealed that dependability of scores can be improved by including a second site observer, or by revisiting a site. There is a small additional benefit from increasing observers or visits beyond two.

The use of bedside electronic medical record to improve quality of care in nursing facilities: a qualitative analysis

It appears that the implementation and use of a bedside electronic medical record in nursing homes can be a strategy to improve quality of care. Staff like using the bedside electronic medical record and believe it is beneficial. Information gleaned from this qualitative evaluation of four nursing homes that implemented complete electronic medical records and participated in a larger evaluation of the use of an electronic medical record will be useful to other nursing homes as they consider implementing bedside computing technology. Nursing home owners and administrators must be prepared to undertake a major change requiring many months of planning to successfully implement. Direct care staff will need support as they learn to use the equipment, especially for thefirst 6 to 12 months after implementation. There should be a careful plan for continuing education opportunities so that staff learn to properly use the software and can benefit from the technology. After 12 to 24 months, almost no one wants to return to the era of paper charting.

Initiative to test a multidisciplinary model with advanced practice nurses to reduce avoidable hospitalizations among nursing facility residents.

Author Affiliations: Sinclair School of Nursing (Drs Rantz, Alexander, Vogelsmeier, Popejoy, Flesner, and Crecelius and Ms Lueckenotte), School of Social Work, College of Human and Environmental Sciences (Dr Galambos), and Curtis W. and Ann H. Long Department of Family and Community Medicine (Dr Koopman), University of Missouri, Columbia; and College of Nursing and Health Sciences, University of Wisconsin–Eau Claire (Dr Zwygart-Stauffacher).