Social media use and mental health among older adults with multimorbidity: the role of self-care efficacy.

OBJECTIVES: To describe the prevalence and trends in the use of social media over time and explore whether social media use is related to better self-care efficacy and thus related to better mental health among United States older adults with multimorbidity. MATERIALS AND METHODS: Respondents aged 65 years+ and having 2 or more chronic conditions from the 2017-2020 Health Information National Trends Survey were analyzed (N = 3341) using weighted descriptive and logistic regression analyses. RESULTS: Overall, 48% (n = 1674) of older adults with multimorbidity used social media and there was a linear trend in use over time, increasing from 41.1% in 2017 to 46.5% in 2018, and then further up to 51.7% in 2019, and 54.0% in 2020. Users were often younger, married/partnered, and non-Hispanic White with high education and income. Social media use was associated with better self-care efficacy that was further related to better mental health, indicating a significant mediation effect of self-care efficacy in the relationship between social media use and mental health. DISCUSSION: Although older adults with multimorbidity are a fast-growing population using social media for health, significant demographic disparities exist. While social media use is promising in improving self-care efficacy and thus mental health, relying on social media for the management of multimorbidity might be potentially harmful to those who are not only affected by multimorbidity but also socially disadvantaged (eg, non-White with lower education). CONCLUSION: Great effort is needed to address the demographic disparity and ensure health equity when using social media for patient care.

A Scalable and Extensible Logical Data Model of Electronic Health Record Audit Logs for Temporal Data Mining (RNteract): Model Conceptualization and Formulation.

BACKGROUND: Increased workload, including workload related to electronic health record (EHR) documentation, is reported as a main contributor to nurse burnout and adversely affects patient safety and nurse satisfaction. Traditional methods for workload analysis are either administrative measures (such as the nurse-patient ratio) that do not represent actual nursing care or are subjective and limited to snapshots of care (eg, time-motion studies). Observing care and testing workflow changes in real time can be obstructive to clinical care. An examination of EHR interactions using EHR audit logs could provide a scalable, unobtrusive way to quantify the nursing workload, at least to the extent that nursing work is represented in EHR documentation. EHR audit logs are extremely complex; however, simple analytical methods cannot discover complex temporal patterns, requiring use of state-of-the-art temporal data-mining approaches. To effectively use these approaches, it is necessary to structure the raw audit logs into a consistent and scalable logical data model that can be consumed by machine learning (ML) algorithms. OBJECTIVE: We aimed to conceptualize a logical data model for nurse-EHR interactions that would support the future development of temporal ML models based on EHR audit log data. METHODS: We conducted a preliminary review of EHR audit logs to understand the types of nursing-specific data captured. Using concepts derived from the literature and our previous experience studying temporal patterns in biomedical data, we formulated a logical data model that can describe nurse-EHR interactions, the nurse-intrinsic and situational characteristics that may influence those interactions, and outcomes of relevance to the nursing workload in a scalable and extensible manner. RESULTS: We describe the data structure and concepts from EHR audit log data associated with nursing workload as a logical data model named RNteract. We conceptually demonstrate how using this logical data model could support temporal unsupervised ML and state-of-the-art artificial intelligence (AI) methods for predictive modeling. CONCLUSIONS: The RNteract logical data model appears capable of supporting a variety of AI-based systems and should be generalizable to any type of EHR system or health care setting. Quantitatively identifying and analyzing temporal patterns of nurse-EHR interactions is foundational for developing interventions that support the nursing documentation workload and address nurse burnout.

Medication for Opioid Use Disorder During Pregnancy - Maternal and Infant Network to Understand Outcomes Associated with Use of Medication for Opioid Use Disorder During Pregnancy (MAT-LINK), 2014-2021.

Problem: Medication for opioid use disorder (MOUD) is recommended for persons with opioid use disorder (OUD) during pregnancy. However, knowledge gaps exist about best practices for management of OUD during pregnancy and these data are needed to guide clinical care. Period Covered: 2014-2021. Description of the System: Established in 2019, the Maternal and Infant Network to Understand Outcomes Associated with Medication for Opioid Use Disorder During Pregnancy (MAT-LINK) is a surveillance network of seven clinical sites in the United States. Boston Medical Center, Kaiser Permanente Northwest, The Ohio State University, and the University of Utah were the initial clinical sites in 2019. In 2021, three clinical sites were added to the network (the University of New Mexico, the University of Rochester, and the University of South Florida). Persons receiving care at the seven clinical sites are diverse in terms of geography, urbanicity, race and ethnicity, insurance coverage, and type of MOUD received. The goal of MAT-LINK is to capture demographic and clinical information about persons with OUD during pregnancy to better understand the effect of MOUD on outcomes and, ultimately, provide information for clinical care and public health interventions for this population. MAT-LINK maintains strict confidentiality through robust information technology architecture. MAT-LINK surveillance methods, population characteristics, and evaluation findings are described in this inaugural surveillance report. This report is the first to describe the system, presenting detailed information on funding, structure, data elements, and methods as well as findings from a surveillance evaluation. The findings presented in this report are limited to selected demographic characteristics of pregnant persons overall and by MOUD treatment status. Clinical and outcome data are not included because data collection and cleaning have not been completed; initial analyses of clinical and outcome data will begin in 2023. Results: The MAT-LINK surveillance network gathered data on 5,541 reported pregnancies with a known pregnancy outcome during 2014-2021 among persons with OUD from seven clinical sites. The mean maternal age was 29.7 (SD = ±5.1) years. By race and ethnicity, 86.3% of pregnant persons were identified as White, 25.4% as Hispanic or Latino, and 5.8% as Black or African American. Among pregnant persons, 81.6% had public insurance, and 84.4% lived in urban areas. Compared with persons not receiving MOUD during pregnancy, those receiving MOUD during pregnancy were more likely to be older and White and to have public insurance. The evaluation of the surveillance system found that the initial four clinical sites were not representative of demographics of the South or Southwest regions of the United States and had low representation from certain racial and ethnic groups compared with the overall U.S. population; however, the addition of three clinical sites in 2021 made the surveillance network more representative. Automated extraction and processing improved the speed of data collection and analysis. The ability to add new clinical sites and variables demonstrated the flexibility of MAT-LINK. Interpretation: MAT-LINK is the first surveillance system to collect comprehensive, longitudinal data on pregnant person-infant dyads with perinatal outcomes associated with MOUD during pregnancy from multiple clinical sites. Analyses of clinical site data demonstrated different sociodemographic characteristics between the MOUD and non-MOUD treatment groups. Public Health Actions: MAT-LINK is a timely and flexible surveillance system with data on approximately 5,500 pregnancies. Ongoing data collection and analyses of these data will provide information to support clinical and public health guidance to improve health outcomes among pregnant persons with OUD and their children.

What is the relationship between falls, functional limitations, and depressive symptoms among Chinese older adults? The role of living alone.

OBJECTIVES: Although the association between falls and depressive symptoms is well documented, the mechanisms underlying this association remain largely unexplored. We investigated the mediation role of functional limitations in the association between falls and depressive symptoms among Chinese older adults and determined whether the living arrangement (living alone or not) is a significant moderator of the above-mentioned mediation pathway. STUDY DESIGN: Cross-sectional study. MAIN OUTCOME MEASURES: Depressive symptoms were measured using the 10-item Center for Epidemiologic Studies Depression Scale short form (CESD-10), on which higher scores indicate higher levels of depressive symptoms. RESULTS: We used the harmonized China Health and Retirement Longitudinal Study national baseline data (2011-2012 year) involving 7410 participants aged 60 years and over. After adjusting for covariates (e.g., age and sex), the effects of falls on depressive symptoms were seen to be mediated by functional limitations among Chinese older adults (ß = 0.82, p < .001). The moderated mediation analysis, which assesses whether an indirect effect is conditional on values of a moderating variable, found that the mediation effect was contingent upon the living arrangement (ß = -0.60, p = .029). Specifically, the levels of functional limitations and depressive symptoms were higher for people with falls who were living with others relative to those living alone. CONCLUSIONS: These results suggest that functional limitations are an important intervening variable that links falls to depressive symptoms among Chinese older adults. Interventions to promote older adults' physical function and prevent falls are recommended to decrease the risk of depressive symptoms. These interventions can particularly benefit those who live with others.

Demonstration Project: Transitioning a Research Network to New Single IRB Platforms.

Since the 2016 National Institutes of Health (NIH) mandate to use a single IRB (sIRB) in multicenter research, institutions have struggled to operationalize the process. In this demonstration project, the University of Utah Trial Innovation Center assisted the Collaborative Pediatric Critical Care Research Network to transition from using individually negotiated reliance agreements and paper-based documentation to a new sIRB master agreement and an informatics platform to capture reliance documentation. Lessons learned that can guide other academic institutions and IRBs as they operationalize sIRBs included the need for sites to understand what type of engagement or reliance is required and their need to understand the difference between reliance and activation. Requirements around local review remain poorly understood. Further research is needed to determine approaches that can achieve the NIH vision of reviews becoming more efficient and improving study start-up times, relieving administrative burden while advancing human research protections.

Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy.

How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.

Consent Builder: an innovative tool for creating research informed consent documents.

Objective: To describe process innovations related to research informed consent documents, and development and formative evaluation of Consent Builder, a platform for generating consent documents for multicenter studies. Materials and Methods: Analysis of Institutional Review Board workflows and documents, followed by process redesign, document redesign, and software development. Locally developed software leverages REDCap and LaTeX. A small-scale usability study was conducted. Results: Process innovations were combining document types, and conceptualizing 2-part informed consent documents: part 1 standardizing the study description and part 2 with local site verbiage. Consent Builder was implemented in the Trial Innovation Network. User survey scores were acceptable; but areas for improvement were noted. LaTeX coding was the biggest challenge for users. Discussion: The process changes were generally well accepted. The software implementation uncovered un-accounted for assumptions, and variability in IRB review workflow across centers. Technical modifications may be needed before widespread implementation. Conclusion: We demonstrated proof-of-concept of an approach to generate research consent documents that are consistent across sites in study description, but which allow for customization of local site verbiage. The Consent Builder tool is an example of an operational innovation, helping meet a need that arose in part due to regulations around use of Single IRB for multicenter trials.

A Scoping Review and Content Analysis of Common Depressive Symptoms of Young People.

School nurses are the most accessible health care providers for many young people including adolescents and young adults. Early identification of depression results in improved outcomes, but little information is available comprehensively describing depressive symptoms specific to this population. The aim of this study was to develop a taxonomy of depressive symptoms that were manifested and described by young people based on a scoping review and content analysis. Twenty-five journal articles that included narrative descriptions of depressive symptoms in young people were included. A total of 60 depressive symptoms were identified and categorized into five dimensions: behavioral (n = 8), cognitive (n = 14), emotional (n = 15), interpersonal (n = 13), and somatic (n = 10). This comprehensive depression symptom taxonomy can help school nurses to identify young people who may experience depression and will support future research to better screen for depression.

Provider Preferences for Patient-Generated Health Data Displays in Pediatric Asthma: A Participatory Design Approach.

OBJECTIVE: There is a lack of evidence on how to best integrate patient-generated health data (PGHD) into electronic health record (EHR) systems in a way that supports provider needs, preferences, and workflows. The purpose of this study was to investigate provider preferences for the graphical display of pediatric asthma PGHD to support decisions and information needs in the outpatient setting. METHODS: In December 2019, we conducted a formative evaluation of information display prototypes using an iterative, participatory design process. Using multiple types of PGHD, we created two case-based vignettes for pediatric asthma and designed accompanying displays to support treatment decisions. Semi-structured interviews and questionnaires with six participants were used to evaluate the display usability and determine provider preferences. RESULTS: We identified provider preferences for display features, such as the use of color to indicate different levels of abnormality, the use of patterns to trend PGHD over time, and the display of environmental data. Preferences for display content included the amount of information and the relationship between data elements. CONCLUSION: Overall, provider preferences for PGHD include a desire for greater detail, additional sources, and visual integration with relevant EHR data. In the design of PGHD displays, it appears that the visual synthesis of multiple PGHD elements facilitates the interpretation of the PGHD. Clinicians likely need more information to make treatment decisions when PGHD displays are introduced into practice. Future work should include the development of interactive interface displays with full integration of PGHD into EHR systems.

Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions.

Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention-the starting point for delivery of "All the right care, but only the right care," an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.

Anemia Management in Rural Haitian Children: A Mixed Methods Study.

INTRODUCTION: We examined factors influencing anemia outcomes in rural children following implementation of a prevention program. METHOD: Mixed methods study of children, parents, and clinicians utilized statistical modeling and content/ethnographic analysis. Retrospective chart abstraction evaluated treatments administered and measured hemoglobin in children aged 6 to 59 months (n = 161). Prospective interviews/questionnaires examined parent (n = 51) and clinician (n = 19) perceptions. RESULTS: Anemia prevalence decreased by 21.2%. Predictors of increased hemoglobin were clinic visit number and age at first visit. Once anemia improved, children were likely to remain improved (P = .65). Despite favorable program perceptions, stakeholders emphasized ecological barriers, including social disadvantage and local practices. DISCUSSION: Socioeconomic factors prevented guideline concordant behaviors. Persistent attention to intrapersonal, interpersonal, and community social determinants is a sine qua non for successfully managing the epidemic. The first step to provide culturally congruent care is to explicitly acknowledge that guideline-concordant behaviors are often complex.

Patient-Generated Health Data in Pediatric Asthma: Exploratory Study of Providers' Information Needs.

BACKGROUND: Adolescents are using mobile health apps as a form of self-management to collect data on symptoms, medication adherence, and activity. Adding functionality to an electronic health record (EHR) to accommodate disease-specific patient-generated health data (PGHD) may support clinical care. However, little is known on how to incorporate PGHD in a way that informs care for patients. Pediatric asthma, a prevalent health issue in the United States with 6 million children diagnosed, serves as an exemplar condition to examine information needs related to PGHD. OBJECTIVE: In this study we aimed to identify and prioritize asthma care tasks and decisions based on pediatric asthma guidelines and identify types of PGHD that might support the activities associated with the decisions. The purpose of this work is to provide guidance to mobile health app developers and EHR integration. METHODS: We searched the literature for exemplar asthma mobile apps and examined the types of PGHD collected. We identified the information needs associated with each decision in accordance with consensus-based guidelines, assessed the suitability of PGHD to meet those needs, and validated our findings with expert asthma providers. RESULTS: We mapped guideline-derived information needs to potential PGHD types and found PGHD that may be useful in meeting information needs. Information needs included types of symptoms, symptom triggers, medication adherence, and inhaler technique. Examples of suitable types of PGHD were Asthma Control Test calculations, exposures, and inhaler use. Providers suggested uncontrolled asthma as a place to focus PGHD efforts, indicating that they preferred to review PGHD at the time of the visit. CONCLUSIONS: We identified a manageable list of information requirements derived from clinical guidelines that can be used to guide the design and integration of PGHD into EHRs to support pediatric asthma management and advance mobile health app development. Mobile health app developers should examine PGHD information needs to inform EHR integration efforts.

Predicting Falls Among Community-Dwelling Older Adults: A Demonstration of Applied Machine Learning.

Data science skills are increasingly needed by informatics nurses and nurse scientists, but techniques such as machine learning can be daunting for those with clinical, rather than computer science or technical, backgrounds. With the increasing quantity of publicly available population-level datasets, identification of factors that predict clinical outcomes is possible using machine learning algorithms. This study demonstrates how to apply a machine learning approach to nursing-relevant questions, specifically an approach to predict falls among community-dwelling older adults, based on data from the 2014 Behavioral Risk Factor Surveillance System. A random forest algorithm, a common approach to machine learning, was compared to a logistic regression model. Explanations of how to interpret the models and their associated performance characteristics are included to serve as a tutorial to readers. Machine learning methods constitute an increasingly important approach for nursing as population-level data are increasingly being made available to the public.

Loneliness as a mediator of the impact of social isolation on cognitive functioning of Chinese older adults.

BACKGROUND AND OBJECTIVE: older adults have increased risk of social isolation, loneliness and cognitive functioning impairment, but the relationships among these factors are not conclusive. We investigated the potential mediation mechanism of loneliness on the association between social isolation and cognitive functioning among Chinese older adults within their cultural context. DESIGN: secondary analysis of the baseline wave (2011-12) of the harmonised China Health and Retirement Longitudinal Study. SETTING AND SUBJECTS: community-dwelling older adults in China (N = 7,410 participants aged 60-101 years). METHODS: we applied a multiple indicator multiple cause approach to determine whether the construct of social isolation is well defined by four indicators (social activity engagement, weekly adult children contact, caregiving for grandchildren and living alone) and used structural equation modelling to examine the direct and indirect effects among variables of interest. RESULTS: the results demonstrated that social activity engagement, weekly adult children contact and caregiving for grandchildren were significantly related to social isolation (ß = -0.26 to -0.28) (Living alone was fixed to 1 for model identification.) The indirect effect of social isolation on cognitive functioning through loneliness was significant (ß = -0.15), indicating loneliness was an important mediator. However, the direct effect of social isolation on cognitive functioning also remained significant (ß = -0.83), suggesting a partial mediation effect. CONCLUSIONS: our study highlights the mediation role of loneliness in the relationship between social isolation and cognitive functioning among Chinese older adults. The findings support the beneficial effects of maintaining social relations and coping with feelings of loneliness on older adults' cognitive functioning.

Patient-generated health data and electronic health record integration: a scoping review.

OBJECTIVES: Patient-generated health data (PGHD) are clinically relevant data captured by patients outside of the traditional care setting. Clinical use of PGHD has emerged as an essential issue. This study explored the evidence to determine the extent of and describe the characteristics of PGHD integration into electronic health records (EHRs). METHODS: In August 2019, we conducted a systematic scoping review. We included studies with complete, partial, or in-progress PGHD and EHR integration within a clinical setting. The retrieved articles were screened for eligibility by 2 researchers, and data from eligible articles were abstracted, coded, and analyzed. RESULTS: A total of 19 studies met inclusion criteria after screening 9463 abstracts. Most of the study designs were pilots and all were published between 2013 and 2019. Types of PGHD were biometric and patient activity (57.9%), questionnaires and surveys (36.8%), and health history (5.3%). Diabetes was the most common patient condition (42.1%) for PGHD collection. Active integration (57.9%) was slightly more common than passive integration (31.6%). We categorized emergent themes into the 3 steps of PGHD flow. Themes emerged concerning resource requirements, data delivery to the EHR, and preferences for review. DISCUSSION: PGHD integration into EHRs appears to be at an early stage. PGHD have the potential to close health care gaps and support personalized medicine. Efforts are needed to understand how to optimize PGHD integration into EHRs considering resources, standards for EHR delivery, and clinical workflows.

The role of computer-based clinical decision support systems to deliver protective mechanical ventilation.

PURPOSE OF REVIEW: Mechanical ventilation of adults and children with acute respiratory failure necessitates balancing lung and diaphragm protective ventilation. Computerized decision support (CDS) offers advantages in circumstances where complex decisions need to be made to weigh potentially competing risks, depending on the physiologic state of the patient. RECENT FINDINGS: Significant variability in how ventilator protocols are applied still exists and clinical data show that there continues to be wide variability in ventilator management. We have developed a CDS, which we are currently testing in a Phase II randomized controlled trial. The CDS is called Real-time Effort Driven ventilator management (REDvent). We will describe the rationale and methods for development of CDS for lung and diaphragm protective ventilation, using the REDvent CDS as an exemplar. SUMMARY: Goals for achieving compliance and physiologic objectives can be met when CDS instructions are simple and explicit, provide the clinician with the underlying rule set, permit acceptable reasons for declining and allow for iterative adjustments.

Patient-generated health data and electronic health record integration: protocol for a scoping review.

INTRODUCTION: The objective of this study is to determine the extent and describe the nature of patient-generated health data (PGHD) integration into electronic health records (EHRs) using systematic scoping methods to review the available literature. PGHD have the potential to enhance decision making by providing the valuable information that may not be ordinarily captured during a routine care visit. These data which are captured from mobile devices, such as smartphones, activity trackers and other sensors, should be integrated into clinical workflows to allow for optimal use by clinicians. METHODS AND ANALYSIS: This study aims to conduct a rigorous scoping review to explore evidence related to the integration of PGHD into EHRs. Using the framework developed by Arksey and O'Malley, we will create a systematic search strategy, chart data from the relevant articles, and use a qualitative, thematic approach to analyse the data. This review will enable the identification of types of integration and describe challenges and barriers to integrating PGHD. ETHICS AND DISSEMINATION: Database searches will be initiated in June 2019. The review is expected to be completed by October 2019. As the content of the full-text articles emerges, the authors will summarise the characteristics related to the integration of PGHD. The findings of this scoping review will identify research gaps and present implications for future research.

Navigating the Search for Patient Generated Health Data.

With massive amounts of mobile health data generated by patients, there is a growing amount of research conducted to understand their impact on patient care. The MeSH heading for patient generated health data was established in early 2018, complicating searches for PGHD research prior to 2018. In conducting a search of scientific databases, keywords are presented along with their degree of representation in the literature to help inform future searches.

Using Temporal Features to Provide Data-Driven Clinical Early Warnings for Chronic Obstructive Pulmonary Disease and Asthma Care Management: Protocol for a Secondary Analysis.

BACKGROUND: Both chronic obstructive pulmonary disease (COPD) and asthma incur heavy health care burdens. To support tailored preventive care for these 2 diseases, predictive modeling is widely used to give warnings and to identify patients for care management. However, 3 gaps exist in current modeling methods owing to rarely factoring in temporal aspects showing trends and early health change: (1) existing models seldom use temporal features and often give late warnings, making care reactive. A health risk is often found at a relatively late stage of declining health, when the risk of a poor outcome is high and resolving the issue is difficult and costly. A typical model predicts patient outcomes in the next 12 months. This often does not warn early enough. If a patient will actually be hospitalized for COPD next week, intervening now could be too late to avoid the hospitalization. If temporal features were used, this patient could potentially be identified a few weeks earlier to institute preventive therapy; (2) existing models often miss many temporal features with high predictive power and have low accuracy. This makes care management enroll many patients not needing it and overlook over half of the patients needing it the most; (3) existing models often give no information on why a patient is at high risk nor about possible interventions to mitigate risk, causing busy care managers to spend more time reviewing charts and to miss suited interventions. Typical automatic explanation methods cannot handle longitudinal attributes and fully address these issues. OBJECTIVE: To fill these gaps so that more COPD and asthma patients will receive more appropriate and timely care, we will develop comprehensible data-driven methods to provide accurate early warnings of poor outcomes and to suggest tailored interventions, making care more proactive, efficient, and effective. METHODS: By conducting a secondary data analysis and surveys, the study will: (1) use temporal features to provide accurate early warnings of poor outcomes and assess the potential impact on prediction accuracy, risk warning timeliness, and outcomes; (2) automatically identify actionable temporal risk factors for each patient at high risk for future hospital use and assess the impact on prediction accuracy and outcomes; and (3) assess the impact of actionable information on clinicians' acceptance of early warnings and on perceived care plan quality. RESULTS: We are obtaining clinical and administrative datasets from 3 leading health care systems' enterprise data warehouses. We plan to start data analysis in 2020 and finish our study in 2025. CONCLUSIONS: Techniques to be developed in this study can boost risk warning timeliness, model accuracy, and generalizability; improve patient finding for preventive care; help form tailored care plans; advance machine learning for many clinical applications; and be generalized for many other chronic diseases. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/13783.