Usability Testing of the PACE-App to Support Family Caregivers in Managing Pain for People With Dementia.

Background And Objectives: Pain management is often suboptimal in individuals with dementia, and their family caregivers are tasked with supporting pain management despite limited preparation. The web-based PACE-app (PAin Control Enhancement) was designed to assist caregivers in managing pain for individuals with dementia. This study aimed to evaluate the usability of the PACE-app.

How Do Nurses Assess Cognition in Adults With Neurological Conditions? A Scoping Review.

Background: According to epidemiological studies, neurological cognitive problems are increasingly prevalent in the aging population, with estimates that the number of people living with cognitive impairment will triple by 2050. Therefore, early detection in rehabilitation settings is needed to manage cognitive changes to ensure that individuals living with these conditions receive care and support that addresses their needs.

Purpose: This scoping review, based on the Arksey and O'Malley method, aims to investigate the cognitive assessments used for patients with neurological conditions in current nursing practice.

Unraveling the multiple chronic conditions patterns among people with Alzheimer's disease and related dementia: A machine learning approach to incorporate synergistic interactions.

Introduction: Most people with Alzheimer's disease and related dementia (ADRD) also suffer from two or more chronic conditions, known as multiple chronic conditions (MCC). While many studies have investigated the MCC patterns, few studies have considered the synergistic interactions with other factors (called the syndemic factors) specifically for people with ADRD.

Methods: We included 40,290 visits and identified 18 MCC from the National Alzheimer's Coordinating Center.

Detecting Multimorbidity Patterns with Association Rule Mining in Patients with Alzheimer's Disease and Related Dementias.

Researchers estimate the number of dementia patients to triple by 2050. Dementia seldom occurs in isolation; it's frequently accompanied by other health conditions. The coexistence of conditions further complicates the management of dementia.

Potential impact of blood cholesterol guidelines on statin treatment in the U.S. population using interrupted time series analysis.

Background: The 2013 ACC/AHA Guideline was a paradigm shift in lipid management and identified the four statin-benefit groups. Many have studied the guideline's potential impact, but few have investigated its potential long-term impact on MACE. Furthermore, most studies also ignored the confounding effect from the earlier release of generic atorvastatin in Dec 2011.

Measuring the Impact of Primary Care Team Composition on Patient Activation Utilizing Electronic Health Record Big Data Analytics.

Purpose: Team-based care has been linked to key outcomes associated with the Quadruple Aim and a key driver of high-value patient-centered care. Use of the electronic health record (EHR) and machine learning have significant potential to overcome previous barriers to studying the impact of teams, including delays in accessing data to improve teamwork and optimize patient outcomes.

Methods: This study utilized a large EHR dataset (n=316,542) from an urban health system to explore the relationship between team composition and patient activation, a key driver of patient engagement.

Development and Validation of the Pharmacological Statin-Associated Muscle Symptoms Risk Stratification Score Using Electronic Health Record Data.

Statin-associated muscle symptoms (SAMS) can lead to statin nonadherence. This paper aims to develop a pharmacological SAMS risk stratification (PSAMS-RS) score using a previously developed PSAMS phenotyping algorithm that distinguishes objective vs. nocebo SAMS using electronic health record (EHR) data.

Decision rules for personalized statin treatment prescriptions over multi-objectives.

In our previous study, we demonstrated the feasibility of producing a proactive statin prescription strategy - a personalized statin treatment plan (PSTP) - using neural networks with big data. However, its non-transparency limited result interpretations and clinical usability. To improve the transparency of our previous approach with minimal compromise to the maximal statin treatment benefit-to-risk ratio, this study proposed a five-step pipeline approach called the decision rules for statin treatment (DRST).

Development and application of pharmacological statin-associated muscle symptoms phenotyping algorithms using structured and unstructured electronic health records data.

Importance: Statins are widely prescribed cholesterol-lowering medications in the United States, but their clinical benefits can be diminished by statin-associated muscle symptoms (SAMS), leading to discontinuation.

Objectives: In this study, we aimed to develop and validate a pharmacological SAMS clinical phenotyping algorithm using electronic health records (EHRs) data from Minnesota Fairview.

Materials And Methods: We retrieved structured and unstructured EHR data of statin users and manually ascertained a gold standard set of SAMS cases and controls using the published SAMS-Clinical Index tool from clinical notes in 200 patients.

Development and validation of the pharmacological statin-associated muscle symptoms risk stratification (PSAMS-RS) score using real-world electronic health record data.

Introduction: Statin-associated muscle symptoms (SAMS) contribute to the nonadherence to statin therapy. In a previous study, we successfully developed a pharmacological SAMS (PSAMS) phenotyping algorithm that distinguishes objective versus nocebo SAMS using structured and unstructured electronic health records (EHRs) data. Our aim in this paper was to develop a pharmacological SAMS risk stratification (PSAMS-RS) score using these same EHR data.

Using data-driven analytics and ecological systems theory to identify risk and protective factors for school absenteeism among secondary students.

Chronic absenteeism is an administrative term defining extreme failure for students to be present at school, which can have devastating long-term impacts on students. Although numerous prior studies have investigated associated variables and interventions, there are few studies that utilize both theory-driven and data-informed approaches to investigate absenteeism. The current study applied data-driven machine learning techniques, grounded in "The Kids and Teens at School" (KiTeS) theoretical framework, to student-level data (N = 121,005) to identify risk and protective variables that are highly associated with school absences.

Development and Application of Pharmacological Statin-Associated Muscle Symptoms Phenotyping Algorithms Using Structured and Unstructured Electronic Health Records Data.

Background: Statins are widely prescribed cholesterol-lowering medications in the US, but their clinical benefits can be diminished by statin-associated muscle symptoms (SAMS), leading to discontinuation. In this study, we aimed to develop and validate a pharmacological SAMS clinical phenotyping algorithm using electronic health records (EHRs) data from Minnesota Fairview.

Methods: We retrieved structured and unstructured EHR data of statin users and manually ascertained a gold standard set of SAMS cases and controls using the SAMS-CI tool from clinical notes in 200 patients.

Using artificial intelligence to improve pain assessment and pain management: a scoping review.

Context: Over 20% of US adults report they experience pain on most days or every day. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain using artificial intelligence (AI) approaches may improve patient outcomes and healthcare resource utilization.

Comparison of the Warfarin Dosing and Outcomes in Hmong Versus East Asians Patients: Real-World Data From an Integrated Healthcare System.

Background Previous research predicted that Hmong, an understudied East Asian subpopulation, might require significantly lower warfarin doses than East Asian patients partially due to their unique genetic and clinical factors. However, such findings have not been corroborated using real-world data. Methods This was a retrospective cohort study of Hmong and East Asian patients receiving warfarin.

Clinical Data Cohort Quality Improvement: The Case of the Medication Data in The University of Minnesota's Clinical Data Repository.

Clinical and translational research centers (CTRCs) have emerged as key centers for electronic medical record related research through integrated data repositories (IDRs) and the 'secondary use' of clinical data. Researchers accessing and pre-processing ever increasing amounts of electronic medical records for data mining tasks have a growing need for best practice approaches for clinical data quality assessment and improvement. This project focused on a large data extract for 7 statin medication prescriptions for patients with cardiovascular disease.

Producing personalized statin treatment plans to optimize clinical outcomes using big data and machine learning.

Almost half of Americans 65 years of age and older take statins, which are highly effective in lowering low-density lipoprotein cholesterol, preventing atherosclerotic cardiovascular disease (ASCVD), and reducing all-cause mortality. Unfortunately, ∼50% of patients prescribed statins do not obtain these critical benefits because they discontinue use within one year of treatment initiation. Therefore, statin discontinuation has been identified as a major public health concern due to the increased morbidity, mortality, and healthcare costs associated with ASCVD.

Development, implementation and participant evaluation of combining text messaging and peer group support in a weight management programme for African-American women.

Background: Development of highly accessible interventions that are effective in reducing body weight, preventing weight gain, and maintaining weight loss is urgently needed to solve the current obesity epidemic, especially among African-American women.

Aims: The purpose of this paper is to describe the development, implementation, and participant evaluation processes of a combined text messaging and peer support group programme to enhance weight management skills among African-American women.

Methods: The programme's conceptual framework and operational model were developed to enhance the research design and protocol to support the study rationale and to lay a solid theoretical base for programme implementation.

A Population-Based Study of Simvastatin Drug-Drug Interactions in Cardiovascular Disease Patients.

Simvastatin is a commonly used medication for lipid management and cardiovascular disease, however, the risk of adverse events (AEs) with its use increases via drug-drug interaction (DDI) exposures. Patients were extracted if initially diagnosed with cardiovascular disease and newly initiated simvastatin therapy. The cohort was divided into a DDI-exposed group and a non-DDI exposed group.

Knowledge Discovery With Machine Learning for Hospital-Acquired Catheter-Associated Urinary Tract Infections.

Massive generation of health-related data has been key in enabling the big data science initiative to gain new insights in healthcare. Nursing can benefit from this era of big data science, as there is a growing need for new discoveries from large quantities of nursing data to provide evidence-based care. However, there are few nursing studies using big data analytics.

Big Data Cohort Extraction for Personalized Statin Treatment and Machine Learning.

The creation of big clinical data cohorts for machine learning and data analysis require a number of steps from the beginning to successful completion. Similar to data set preprocessing in other fields, there is an initial need to complete data quality evaluation; however, with large heterogeneous clinical data sets, it is important to standardize the data in order to facilitate dimensionality reduction. This is particularly important for clinical data sets including medications as a core data component due to the complexity of coded medication data.

Exploring Older Adults' Strengths, Problems, and Wellbeing Using De-identified Electronic Health Record Data.

As new data sources including individuals' strengths emerge in electronic health records, such data provide whole-person oriented information to generate integrated knowledge for person-centered practice. The purpose of this study is to describe older adults' strengths and problems within a wellbeing context documented by the Omaha System. The Wellbeing Model is employed as a conceptual framework for wellbeing and is operationalized by the Omaha System Problem Classification Scheme.

Discovering Associations Among Older Adults' Characteristics and Planned Nursing Interventions Using Electronic Health Record Data.

Background And Purpose: Little is known about how nursing assessments of strengths and signs/symptoms inform intervention planning in assisted living communities. The purpose of this study was to discover associations among older adults' characteristics and their planned nursing interventions.

Methods: This study employed a data-driven method, latent class analysis, using existing electronic health record data from a senior living community in the Midwest.

A strengths-based data capture model: mining data-driven and person-centered health assets.

With health care policy directives advancing value-based care, risk assessments and management have permeated health care discourse. The conventional problem-based infrastructure defines what data are employed to build this discourse and how it unfolds. Such a health care model tends to bias data for risk assessment and risk management toward problems and does not capture data about health assets or strengths.

Factors Associated With Healthcare-Acquired Catheter-Associated Urinary Tract Infections: Analysis Using Multiple Data Sources and Data Mining Techniques.

Purpose: The purpose of this study was to identify factors associated with healthcare-acquired catheter-associated urinary tract infections (HA-CAUTIs) using multiple data sources and data mining techniques.

Subjects And Setting: Three data sets were integrated for analysis: electronic health record data from a university hospital in the Midwestern United States was combined with staffing and environmental data from the hospital's National Database of Nursing Quality Indicators and a list of patients with HA-CAUTIs.

Methods: Three data mining techniques were used for identification of factors associated with HA-CAUTI: decision trees, logistic regression, and support vector machines.

Personalized Anticoagulation: Optimizing Warfarin Management Using Genetics and Simulated Clinical Trials.

Background: Clinical trials testing pharmacogenomic-guided warfarin dosing for patients with atrial fibrillation have demonstrated conflicting results. Non-vitamin K antagonist oral anticoagulants are expensive and contraindicated for several conditions. A strategy optimizing anticoagulant selection remains an unmet clinical need.