Objective: 1. Discuss how NASEM and Moving Forward are driving specific and actionable improvements in nursing home quality in the U.S. 2. Identify seven critical goals to achieve high quality care in U.S. nursing homes.The National Academies of Science Engineering and Medicine (NASEM) report The National Imperative to Improve Nursing Home Quality: Honoring our Commitment to Residents
Abstract The Moving Forward Nursing Home Quality Coalition advances the goals outlined by the 2022 National Academies of Sciences, Engineering, and Medicine (NASEM) report aimed at improving nursing home (NH) quality. Over two years, Coalition committee members – along with a network of national leaders, nursing home residents, and the general public – will develop, test and promote action plans that will improve the way the United States finances, delivers, and regulates care in Nursing Homes. The Coalition is comprised of a steering committee and working committees that align with and advance the seven NASEM goals including; 1) advancing delivery of person-centered care; 2) ensuring a well-prepared and compensated workforce; 3) increasing financial transparency and accountability; 4) creating a rational and robust financing system; 5) designing more effective and responsive quality assurance; 6) expanding quality measurement and continuous quality improvement; and 7) adopting health information technology. Coalition committees have engaged in three of four phases of their two-year work plan including convening stakeholders to prioritize and develop action plans to test and promote change. Throughout the work, committee collaboration has ensured action plans are refined and expanded in synergistic ways to shape state and national policy. This presentation will provide an overview of the broader Coalition work and give context for the other presentations which provide an example of collaborative work leading to proposed recommendations for critical policy and regulatory change around person-centered care planning.
People who live in aged care homes have high rates of illness and frailty. Providing evidence-based care to this population is vital to ensure the highest possible quality of life.In this study (CareTrack Aged, CT Aged), we aimed to develop a comprehensive set of clinical indicators for guideline-adherent, appropriate care of commonly managed conditions and processes in aged care.Indicators were formulated from recommendations found through systematic searches of Australian and international clinical practice guidelines (CPGs). Experts reviewed the indicators using a multiround modified Delphi process to develop a consensus on what constitutes appropriate care.From 139 CPGs, 5609 recommendations were used to draft 630 indicators. Clinical experts (n = 41) reviewed the indicators over two rounds. A final set of 236 indicators resulted, mapped to 16 conditions and processes of care. The conditions and processes were admission assessment; bladder and bowel problems; cognitive impairment; depression; dysphagia and aspiration; end of life/palliative care; hearing and vision; infection; medication; mobility and falls; nutrition and hydration; oral and dental care; pain; restraint use; skin integrity and sleep.The suite of CT Aged clinical indicators can be used for research and assessment of the quality of care in individual facilities and across organizations to guide improvement and to supplement regulation or accreditation of the aged care sector. They are a step forward for Australian and international aged care sectors, helping to improve transparency so that the level of care delivered to aged care consumers can be rigorously monitored and continuously improved.
Passive sensor networks were deployed in independent living apartments to monitor older adults in their home environments to detect signs of impending illness and alert clinicians so they can intervene and prevent or delay significant changes in health or functional status. A retrospective qualitative deductive content analysis was undertaken to refine health alerts to improve clinical relevance to clinicians as they use alerts in their normal workflow of routine care delivery to older adults. Clinicians completed written free-text boxes to describe actions taken (or not) as a result of each alert; they also rated the clinical significance (relevance) of each health alert on a scale of 1 to 5. Two samples of the clinician’s written responses to the health alerts were analyzed after alert algorithms had been adjusted based on results of a pilot study using health alerts to enhance clinical decision-making. In the first sample, a total of 663 comments were generated by seven clinicians in response to 385 unique alerts; there are more comments than alerts because more than one clinician rated the same alert. The second sample had a total of 142 comments produced by three clinicians in response to 88 distinct alerts. The overall clinical relevance of the alerts, as judged by the content of the qualitative comments by clinicians for each alert, improved from 33.3% of the alerts in the first sample classified as clinically relevant to 43.2% in the second. The goal is to produce clinically relevant alerts that clinicians find useful in daily practice. The evaluation methods used are described to assist others as they consider building and iteratively refining health alerts to enhance clinical decision making.
The current research includes a psychometric test of a nursing home (NH) health information technology (HIT) maturity survey and staging model. NHs were assembled based on HIT survey scores from a prior study representing NHs with low (20%), medium (60%), and high (20%) HIT scores. Inclusion criteria were NHs that completed at least two annual surveys over 4 years. NH administrators were excluded who participated in the Delphi panel responsible for instrument recommendations. Recruitment occurred from January to May 2019. Administrators from 121 of 429 facilities completed surveys. NHs were characteristically for-profit, medium bed size, and metropolitan. A covariance matrix demonstrated that all dimensions and domains were significantly correlated, except HIT capabilities and integration in administrative activities. Cronbach's alpha was very good (0.86). Principal component analysis revealed all items loaded intuitively onto four components, explaining 80% variance. The HIT maturity survey and staging model can be used to assess nine dimensions and domains, total HIT maturity, and stage, leading to reliable assumptions about NH HIT. [ Research in Gerontological Nursing, 15 (2), 93–99.]
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