Evaluating the Implementation of the Individualized Positive Psychosocial Interaction (IPPI) as a Quality Improvement Project in Nursing Homes.

OBJECTIVES: The Individualized Positive Psychosocial Interaction (IPPI) is a non-pharmacological, person-centered, intervention for nursing home (NH) residents living with moderate to severe dementia. The purpose of this study was to assess the pragmatic implementation of the IPPI by leveraging Ohio's Nursing Home Quality Improvement Program (QIP). METHODS: Implementation teams collected resident mood ratings pre- and post-IPPI and completed virtual interviews to assess their Organizational Readiness for Implementing Change as well as the acceptability, feasibility, and appropriateness of the IPPI. Implementation strategies included: providing ongoing consultation; identifying and preparing champions; assessing for readiness and identifying barriers; and developing and distributing effective educational materials. RESULTS: Fifteen NHs completed the QIP (65% completion rate) and reported high organizational commitment to change and high change efficacy. NHs engaged n = 65 residents in n = 638 IPPIs. Residents experienced a positive mood change after 47% of IPPIs. NHs found the IPPI program to be highly acceptable, feasible, and appropriate. CONCLUSIONS: Overall, 65% of NHs successfully implemented the IPPI QIP with people living with moderate to severe dementia. CLINICAL IMPLICATIONS: Given the positive mood changes and high staff satisfaction, results suggest that these brief, individualized activities can be effective strategies to address the communication of distress among PLWD.

Emotion-focused communication training online: Development and evaluation of acceptability.

Emotion-focused communication can improve the delivery of care for long-term care recipients, especially individuals' living with dementia. The purpose of this manuscript is to describe the process of translating the Emotion-Focused Communication Training (EFCT) for long-term care staff from an in-person workshop to an online program and evaluate its acceptability and impact on knowledge of emotion-focused communication and perceived self-efficacy for utilizing emotion-focused communication. The online course was developed following the ADDIE (Analysis, Design, Development, Implementation, Evaluation) Model. The program teaches participants to better identify and manage their own emotions and others' emotions in care. Pre- and post-training t-test comparisons on data collected from 247 caregivers across disciplines in long-term care (direct care workers, life-enrichment/activities staff, health care providers, case managers, health educators, students, administrators, ombudsmen, dietary, housekeeping, or maintenance staff, human resources employees, family care partners, and others) indicated a significant increase in level of knowledge of emotion-focused communication and confidence in applying the learned knowledge and skills in care (increased self-efficacy). Participants also reported high levels of program acceptability. Overall, findings indicate the benefits of translating an in-person training opportunity for caregivers into an interactive online experience; implications for caregivers and care recipients are discussed.

Evaluating the Implementation of a Pragmatic Person-Centered Communication Tool for the Nursing Home Setting: PAL Cards.

OBJECTIVES: The purpose of this quality improvement project was to evaluate the implementation of a person-centered communication tool in nursing homes (NH). The Preferences for Activity and Leisure (PAL) Cards were developed to communicate residents' preferences for activities across care team members. METHODS: Providers were recruited to assess resident important preferences and create PAL Cards for 15-20 residents and collected data aligned with the RE-AIM framework. RESULTS: Reach and Adoption: A total of 43 providers registered and 26 (60%) providers completed the project. Effectiveness and Implementation: Participants attempted 424 PAL Cards and completed 406. For the 26 providers, the average acceptability of the intervention measure was 4.7 (SD 0.4), intervention appropriateness measure was 4.5 (SD 0.5), and feasibility of intervention measure was 4.6 (SD 0.5) (all out of 5). Maintenance: Providers were able to complete 82% of PAL Card placement over the course of 5 months. CONCLUSIONS: The majority of providers were successful in implementing PAL Cards for residents and reported the intervention as highly acceptable, appropriate, and feasible providing necessary data to inform future effectiveness trials. CLINICAL IMPLICATIONS: The intervention can assist nursing home providers in meeting PCC regulations and contribute to building relationships between residents, family, and staff.

Applying Agile Methodology to Reengineer the Delivery of Person-Centered Care in a Nursing Home: A Case Study.

Nursing home (NH) providers would benefit from adopting evidence-based measures for gathering and utilizing resident preference information in their daily care activities. However, providers face barriers when implementing assessment tools used to promote person-centered care (PCC). Although Agile methodology is not commonly used in NH settings, this case study shows how it can be used to achieve the goal of delivering preference-based, PCC, within a large NH. We present a road map for breaking down care processes, prioritizing, and implementing iterative plan, do, study, act cycles using Agile methodology to enhance group collaboration on quality improvement cycles, to achieve our goal of providing preference-based PCC. We first determined if care plans reflected each resident's important preferences, developed a method for tracking whether residents attended activities that matched their preferences, and determined if residents were satisfied that their preferences were being met. These efforts had positive effects throughout the NH particularly when COVID-19 limited visitors and significantly modified staff workflow. Specifically, Agile processes helped staff to know how to honor preferences during quarantines which necessitated a shift to individualized (and not group) approaches for meeting preferences for social contact, comfort, and belonging. The ready availability of preference-based reporting was critical to quickly informing new staff on how to meet residents' most important preferences. Based on lessons learned, we describe a developmental approach that other providers can consider for adoption. Implications of this work are discussed in terms of the need for provider training in Agile methodologies to support iterative improvements, the need for policies that reimburse providers for their efforts, and additional research around workflow processes.

A Machine Learning Recommender System to Tailor Preference Assessments to Enhance Person-Centered Care Among Nursing Home Residents.

Background and Objectives: Nursing homes (NHs) using the Preferences for Everyday Living Inventory (PELI-NH) to assess important preferences and provide person-centered care find the number of items (72) to be a barrier to using the assessment. Research Design and Methods: Using a sample of n = 255 NH resident responses to the PELI-NH, we used the 16 preference items from the MDS 3.0 Section F to develop a machine learning recommender system to identify additional PELI-NH items that may be important to specific residents. Much like the Netflix recommender system, our system is based on the concept of collaborative filtering whereby insights and predictions (e.g., filters) are created using the interests and preferences of many users. The algorithm identifies multiple sets of "you might also like" patterns called association rules, based upon responses to the 16 MDS preferences that recommends an additional set of preferences with a high likelihood of being important to a specific resident. Results: In the evaluation of the combined apriori and logistic regression approach, we obtained a high recall performance (i.e., the ratio of correctly predicted preferences compared with all predicted preferences and nonpreferences) and high precision (i.e., the ratio of correctly predicted rules with respect to the rules predicted to be true) of 80.2% and 79.2%, respectively. Discussion and Implications: The recommender system successfully provides guidance on how to best tailor the preference items asked of residents and can support preference capture in busy clinical environments, contributing to the feasibility of delivering person-centered care.