Ambulatory Care Coordination Data Gathering and Use.

Care coordination is a crucial component of healthcare systems. However, little is known about data needs and uses in ambulatory care coordination practice. Therefore, the purpose of this study was to identify information gathered and used to support care coordination in ambulatory settings. Survey respondents (33) provided their demographics and practice patterns, including use of electronic health records, as well as data gathered and used. Most of the respondents were nurses, and they described varying practice settings and patterns. Although most described at least partial use of electronic health records, two respondents described paper documentation systems. More than 25% of respondents gathered and used most of the 72 data elements, with collection and use often occurring in multiple locations and contexts. This early study demonstrates significant heterogeneity in ambulatory care coordination data usage. Additional research is necessary to identify common data elements to support knowledge development in the context of a learning health system.

A clinical classification framework for identifying persons with high social and medical needs: The COMPLEXedex-SDH.

BACKGROUND: First-generation algorithms resulted in high-cost features as a representation of need but unintentionally introduced systemic bias based on prior ability to access care. Improved precision health approaches are needed to reduce bias and improve health equity. PURPOSE: To integrate nursing expertise into a clinical definition of high-need cases and develop a clinical classification algorithm for implementing nursing interventions. METHODS: Two-phase retrospective, descriptive cohort study using 2019 data to build the algorithm (n = 19,20,848) and 2021 data to test it in adults ≥18 years old (n = 15,99,176). DISCUSSION: The COMPLEXedex-SDH algorithm identified the following populations: cross-cohort needs (10.9%); high-need persons (cross-cohort needs and other social determinants) (17.7%); suboptimal health care utilization for persons with medical complexity (13.8%); high need persons with suboptimal health care utilization (6.2%). CONCLUSION: The COMPLEXedex-SDH enables the identification of high-need cases and value-based utilization into actionable cohorts to prioritize outreach calls to improve health equity and outcomes.

Expanding the evidence for cross-sector collaboration in implementation science: creating a collaborative, cross-sector, interagency, multidisciplinary team to serve patients experiencing homelessness and medical complexity at hospital discharge.

Introduction: Patients with medical and social complexity require care administered through cross-sector collaboration (CSC). Due to organizational complexity, biomedical emphasis, and exacerbated needs of patient populations, interventions requiring CSC prove challenging to implement and study. This report discusses challenges and provides strategies for implementation of CSC through a collaborative, cross-sector, interagency, multidisciplinary team model. Methods: A collaborative, cross-sector, interagency, multidisciplinary team was formed called the Buffalo City Mission Recuperative Care Collaborative (RCU Collaborative), in Buffalo, NY, to provide care transition support for people experiencing homelessness at acute care hospital discharge through a medical respite program. Utilizing the Expert Recommendations for Implementing Change (ERIC) framework and feedback from cross-sector collaborative team, implementation strategies were drawn from three validated ERIC implementation strategy clusters: 1) Develop stakeholder relationships; 2) Use evaluative and iterative strategies; 3) Change infrastructure. Results: Stakeholders identified the following factors as the main barriers: organizational culture clash, disparate visions, and workforce challenges related to COVID-19. Identified facilitators were clear group composition, clinical academic partnerships, and strategic linkages to acute care hospitals. Discussion: A CSC interagency multidisciplinary team can facilitate complex care delivery for high-risk populations, such as medical respite care. Implementation planning is critically important when crossing agency boundaries for new multidisciplinary program development. Insights from this project can help to identify and minimize barriers and optimize utilization of facilitators, such as academic partners. Future research will address external organizational influences and emphasize CSC as central to interventions, not simply a domain to consider during implementation.

Identifying High-Need Primary Care Patients Using Nursing Knowledge and Machine Learning Methods.

BACKGROUND: Patient cohorts generated by machine learning can be enhanced with clinical knowledge to increase translational value and provide a practical approach to patient segmentation based on a mix of medical, behavioral, and social factors. OBJECTIVES: This study aimed to generate a pragmatic example of how machine learning could be used to quickly and meaningfully cohort patients using unsupervised classification methods. Additionally, to demonstrate increased translational value of machine learning models through the integration of nursing knowledge. METHODS: A primary care practice dataset (N = 3,438) of high-need patients defined by practice criteria was parsed to a subset population of patients with diabetes (n = 1233). Three expert nurses selected variables for k-means cluster analysis using knowledge of critical factors for care coordination. Nursing knowledge was again applied to describe the psychosocial phenotypes in four prominent clusters, aligned with social and medical care plans. RESULTS: Four distinct clusters interpreted and mapped to psychosocial need profiles, allowing for immediate translation to clinical practice through the creation of actionable social and medical care plans. (1) A large cluster of racially diverse female, non-English speakers with low medical complexity, and history of childhood illness; (2) a large cluster of English speakers with significant comorbidities (obesity and respiratory disease); (3) a small cluster of males with substance use disorder and significant comorbidities (mental health, liver and cardiovascular disease) who frequently visit the hospital; and (4) a moderate cluster of older, racially diverse patients with renal failure. CONCLUSION: This manuscript provides a practical method for analysis of primary care practice data using machine learning in tandem with expert clinical knowledge.

The Role of Telehealth and Clinical Informatics in Data Driven Primary Care Redesign.

Clinical informatics linked inpatient and emergency department use to clinical data to evaluate utilization for population segments. Trend analysis demonstrates how remote registered nurse care management and the COVID-79 pandemic reduced emergency department utilization in adult populations with high social needs.

Care Coordination: A Concept Analysis.

ABSTRACT: Care coordination is both a well-known concept discussed in a wide range of multidisciplinary health care literature and a familiar nursing role in clinical practice; however, the definition of care coordination lacks role clarity across disciplines and within the nursing profession. Despite variations, defining factors of care coordination practice exist; however, role ambiguity limits the effective implementation of evidence-based care coordination in practice and policy. Following Walker and Avant's eight-step concept analysis method, we aim to further clarify care coordination as a concept and practice role and examine the value that nursing brings to its implementation.

Risk prediction and segmentation models used in the United States for assessing risk in whole populations: a critical literature review with implications for nurses' role in population health management.

OBJECTIVE: We sought to assess the current state of risk prediction and segmentation models (RPSM) that focus on whole populations. MATERIALS: Academic literature databases (ie MEDLINE, Embase, Cochrane Library, PROSPERO, and CINAHL), environmental scan, and Google search engine. METHODS: We conducted a critical review of the literature focused on RPSMs predicting hospitalizations, emergency department visits, or health care costs. RESULTS: We identified 35 distinct RPSMs among 37 different journal articles (n = 31), websites (n = 4), and abstracts (n = 2). Most RPSMs (57%) defined their population as health plan enrollees while fewer RPSMs (26%) included an age-defined population (26%) and/or geographic boundary (26%). Most RPSMs (51%) focused on predicting hospital admissions, followed by costs (43%) and emergency department visits (31%), with some models predicting more than one outcome. The most common predictors were age, gender, and diagnostic codes included in 82%, 77%, and 69% of models, respectively. DISCUSSION: Our critical review of existing RPSMs has identified a lack of comprehensive models that integrate data from multiple sources for application to whole populations. Highly depending on diagnostic codes to define high-risk populations overlooks the functional, social, and behavioral factors that are of great significance to health. CONCLUSION: More emphasis on including nonbilling data and providing holistic perspectives of individuals is needed in RPSMs. Nursing-generated data could be beneficial in addressing this gap, as they are structured, frequently generated, and tend to focus on key health status elements like functional status and social/behavioral determinants of health.

Mortality Risk in Homebound Older Adults Predicted From Routinely Collected Nursing Data.

BACKGROUND: Newer analytic approaches for developing predictive models provide a method of creating decision support to translate findings into practice. OBJECTIVES: The aim of this study was to develop and validate a clinically interpretable predictive model for 12-month mortality risk among community-dwelling older adults. This is done by using routinely collected nursing assessment data to aide homecare nurses in identifying older adults who are at risk for decline, providing an opportunity to develop care plans that support patient and family goals for care. METHODS: A retrospective secondary analysis of Medicare and Medicaid data of 635,590 Outcome and Assessment Information Set (OASIS-C) start-of-care assessments from January 1, 2012, to December 31, 2012, was linked to the Master Beneficiary Summary File (2012-2013) for date of death. The decision tree was benchmarked against gold standards for predictive modeling, logistic regression, and artificial neural network (ANN). The models underwent k-fold cross-validation and were compared using area under the curve (AUC) and other data science metrics, including Matthews correlation coefficient (MCC). RESULTS: Decision tree variables associated with 12-month mortality risk included OASIS items: age, (M1034) overall status, (M1800-M1890) activities of daily living total score, cancer, frailty, (M1410) oxygen, and (M2020) oral medication management. The final models had good discrimination: decision tree, AUC = .71, 95% confidence interval (CI) [.705, .712], sensitivity = .73, specificity = .58, MCC = .31; ANN, AUC = .74, 95% CI [.74, .74], sensitivity = .68, specificity = .68, MCC = .35; and logistic regression, AUC = .74, 95% CI [.735, .742], sensitivity = .64, specificity = .70, MCC = .35. DISCUSSION: The AUC and 95% CI for the decision tree are slightly less accurate than logistic regression and ANN; however, the decision tree was more accurate in detecting mortality. The OASIS data set was useful to predict 12-month mortality risk. The decision tree is an interpretable predictive model developed from routinely collected nursing data that may be incorporated into a decision support tool to identify older adults at risk for death.

Exploring Medicaid claims data to understand predictors of healthcare utilization and mortality for Medicaid individuals with or without a diagnosis of lung cancer: a feasibility study.

Health disparities in low-income populations complicate care for at-risk individuals or those diagnosed with lung cancer and may influence their patterns of healthcare utilization. The purpose of this study is to examine whether age, sex, provider's affiliation, Medicare dual eligibility, and number of comorbidities can predict healthcare utilization, as well as to examine factors influencing mortality in lung biopsy patients. A retrospective review of de-identified Medicaid claims of adults having a lung biopsy in 2013 resulted in classification into lung cancer and non-lung cancer cases based on a lung cancer diagnostic code within 30 days after biopsy. Biopsy cases were further divided by whether or not the provider's institution was accredited by the Commission on Cancer (CoC). Inpatient (IP), outpatient (OP), and emergency department (ED) utilization was followed from initial date of biopsy through 2015, or to the earliest date of death, disenrollment, or study end for both groups. The result of Cox proportional hazards regression model indicated that age and the number of comorbidities significantly predicted OP use and the number of comorbidities significantly predicted ED use in patients with lung cancer. However, for non-lung cancer patients, only the number of comorbidities significantly predicted IP and ED uses. Furthermore, for patients with lung cancer, the significant factors of mortality included IP use per month and the number of comorbidities. Patients with lung cancer who received a lung biopsy by a CoC-accredited organization had a longer time of survival from the biopsy event. Our findings suggest that understanding predictors of healthcare utilization and mortality may create opportunities to improve health and quality of life through better healthcare coordination.

Reducing Emergency Room Visits and In-Hospitalizations by Implementing Best Practice for Transitional Care Using Innovative Technology and Big Data.

BACKGROUND: Efforts to improve care transitions require coordination across the healthcare continuum and interventions that enhance communication between acute and community settings. AIMS: To improve post-discharge utilization value using technology to identify high-risk individuals who might benefit from rapid nurse outreach to assess social and behavioral determinants of health with the goal of reducing inpatient and emergency department visits. METHODS: The project employed a before and after comparison of the intervention site with similar primary care practice sites using population-level Medicaid claims data. The intervention targeted discharged persons with preexisting chronic disease and delivered a care transition alert to a nurse care coordinator for immediate telephonic outreach. The nurse assessed social determinants of health and incorporated problems into the EHR to share across settings. The project evaluated health outcomes and the value of nursing care on existing electronic claims data to compare utilization in the years before and during the intervention using negative binomial regression to account for rare events such as inpatient visits. RESULTS: Avoiding readmissions and emergency visits, and increasing timely outpatient visits improved the individual's experience of care and the work life of healthcare providers, while reducing per capita costs (Quadruple Aim). In the intervention practice, the nurse care coordinator demonstrated the value of nursing care by reducing inpatient (25%) and emergency (35%) visits, and increasing outpatient visits (27%). The estimated value of avoided encounters over the secular Medicaid trend was $664 per adult with chronic disease, generating $71,289 in revenue from additional outpatient visits. LINKING EVIDENCE TO ACTION: Using health information exchange to deliver appropriate and timely evidence-based clinical decision support in the form of care transition alerts and assessment of social determinants of health, in conjunction with data science methods, demonstrates the value of nursing care and resulted in achieving the Quadruple Aim.

Estimating the causal effects of chronic disease combinations on 30-day hospital readmissions based on observational Medicaid data.

Objective: Demonstrate how observational causal inference methods can generate insights into the impact of chronic disease combinations on patients' 30-day hospital readmissions. Materials and Methods: Causal effect estimation was used to quantify the impact of each risk factor scenario (ie, chronic disease combination) associated with chronic kidney disease and heart failure (HF) for adult Medicaid beneficiaries with initial hospitalizations in 2 New York State counties. The experimental protocol: (1) created matched risk factor and comparator groups, (2) assessed covariate balance in the matched groups, and (3) estimated causal effects and their statistical significance. Causality lattices summarized the impact of chronic disease comorbidities on readmissions. Results: Chronic disease combinations were ordered with respect to their causal impact on readmissions. Of disease combinations associated with HF, the combination of HF, coronary artery disease, and tobacco abuse (in that order) had the highest causal effect on readmission rate (+22.3%); of disease combinations associated with chronic kidney disease, the combination of chronic kidney disease, coronary artery disease, and diabetes had the highest effect (+9.5%). Discussion: Multi-hypothesis causal analysis reveals the effects of chronic disease comorbidities on health outcomes. Understanding these effects will guide the development of health care programs that address unique care needs of different patient subpopulations. Additionally, these insights bring new attention to individuals at high risk for readmission based on chronic disease comorbidities, allowing for more personalized attention and prioritization of care. Conclusion: Multi-hypothesis causal analysis, a new methodological tool, generates meaningful insights from health care claims data, guiding the design of care and intervention programs.

Complexity of Chronic Conditions' Impact on End-of-Life Expense Trajectories of Medicare Decedents.

OBJECTIVE: The aim of this study is to determine if the pattern of monthly medical expense can be used to identify individuals at risk of dying, thus supporting providers in proactively engaging in advanced care planning discussions. BACKGROUND: Identifying the right time to discuss end of life can be difficult. Improved predictive capacity has made it possible for nurse leaders to use large data sets to guide clinical decision making. METHODS: We examined the patterns of monthly medical expense of Medicare beneficiaries with life-limiting illness during the last 24 months of life using analysis of variance, t tests, and stepwise hierarchical linear modeling. RESULTS: In the final year of life, monthly medical expense increases rapidly for all disease groupings and forms distinct patterns of change. CONCLUSION: Type of condition can be used to classify decedents into distinctly different cost trajectories. Conditions including chronic disease, system failure, or cancer may be used to identify patients who may benefit from supportive care.

Integrating social context into comprehensive shared care plans: A scoping review.

BACKGROUND: Failure to address social determinants of health (SDH) may contribute to the problem of readmissions in high-risk individuals. Comprehensive shared care plans (CSCP) may improve care continuity and health outcomes by communicating SDH risk factors across settings. PURPOSE: The purpose of this study to evaluate the state of knowledge for integrating SDH into a CSCP. Our scoping review of the literature considered 13,886 articles, of which seven met inclusion criteria. RESULTS: Identified themes were: integrate health and social sectors; interoperability; standardizing ontologies and interventions; process implementation; professional tribalism; and patient centeredness. DISCUSSION: There is an emerging interest in bridging the gap between health and social service sectors. Standardized ontologies and theoretical definitions need to be developed to facilitate communication, indexing, and data retrieval. CONCLUSIONS: We identified a gap in the literature that indicates that foundational work will be required to guide the development of a CSCP that includes SDH that can be shared across settings. The lack of studies published in the United States suggests that this is a critical area for future research and funding.

Integrating Social Determinants of Health into Primary Care Clinical and Informational Workflow during Care Transitions.

CONTEXT: Care continuity during transitions between the hospital and home requires reliable communication between providers and settings and an understanding of social determinants that influence recovery. CASE DESCRIPTION: The coordinating transitions intervention uses real time alerts, delivered directly to the primary care practice for complex chronically ill patients discharged from an acute care setting, to facilitate nurse care coordinator led telephone outreach. The intervention incorporates claims-based risk stratification to prioritize patients for follow-up and an assessment of social determinants of health using the Patient-centered Assessment Method (PCAM). Results from transitional care are stored and transmitted to qualified healthcare providers across the continuum. FINDINGS: Reliance on tools that incorporated interoperability standards facilitated exchange of health information between the hospital and primary care. The PCAM was incorporated into both the clinical and informational workflow through the collaboration of clinical, industry, and academic partners. Health outcomes improved at the study practice over their baseline and in comparison with control practices and the regional Medicaid population. MAJOR THEMES: Current research supports the potential impact of systems approaches to care coordination in improving utilization value after discharge. The project demonstrated that flexibility in developing the informational and clinical workflow was critical in developing a solution that improved continuity during transitions. There is additional work needed in developing managerial continuity across settings such as shared comprehensive care plans. CONCLUSIONS: New clinical and informational workflows which incorporate social determinant of health data into standard practice transformed clinical practice and improved outcomes for patients.

The Roles of Chronic Disease Complexity, Health System Integration, and Care Management in Post-Discharge Healthcare Utilization in a Low-Income Population.

Economically disadvantaged individuals with chronic disease have high rates of in-patient (IP) readmission and emergency department (ED) utilization following initial hospitalization. The purpose of this study was to explore the relationships between chronic disease complexity, health system integration (admission to accountable care organization [ACO] hospital), availability of care management interventions (membership in managed care organization [MCO]), and 90-day post-discharge healthcare utilization. We used de-identified Medicaid claims data from two counties in western New York. The study population was 114,295 individuals who met inclusion criteria, of whom 7,179 had index hospital admissions in the first 9 months of 2013. Individuals were assigned to three disease complexity segments based on presence of 12 prevalent conditions. The 30-day inpatient (IP) readmission rates ranged from 6% in the non-chronic segment to 12% in the chronic disease complexity segment and 21% in the organ system failure complexity segment. Rehospitalization rates (both inpatient and emergency department [ED]) were lower for patients in MCOs and ACOs than for those in fee-for-service care. Complexity of chronic disease, initial hospitalization in a facility that was part of an ACO, MCO membership, female gender, and longer length of stay were associated with a significantly longer time to readmission in the first 90 days, that is, fewer readmissions. Our results add to evidence that high-value post-discharge utilization (fewer IP or ED rehospitalizations and early outpatient follow-up) require population-based transitional care strategies that improve continuity between settings and take into account the illness complexity of the Medicaid population. © 2016 Wiley Periodicals, Inc.

Medical Mondays: ED Utilization for Medicaid Recipients Depends on the Day of the Week, Season, and Holidays.

UNLABELLED: The purpose of this study is to describe and explain the temporal and seasonal trends in ED utilization for a low-income population. METHODS: A retrospective analysis of 66,487 ED Medicaid-insured health care claims in 2009 was conducted for 2 Western New York Counties using time-series analysis with autoregressive moving average (ARMA) models. RESULTS: The final ARMA (2,0) model indicated an autoregressive structure with up to a 2-day lag. ED volume is lower on weekends than on weekdays, and the highest volumes are on Mondays. Summer and fall seasons demonstrated higher volumes, whereas lower volume outliers were associated with holidays. DISCUSSION: Day of the week was an influential predictor of ED utilization in low-income persons. Season and holidays are also predictors of ED utilization. These calendar-based patterns support the need for ongoing and future emergency leaders' collaborations in community-based care system redesign to meet the health care access needs of low-income persons.

Comparative Effectiveness of Risk-Stratified Care Management in Reducing Readmissions in Medicaid Adults With Chronic Disease.

Hospitalized adult Medicaid recipients with chronic disease are at risk for rehospitalization within 90 days of discharge, but most research has focused on the Medicare population. The purpose of this study is to examine the impact of population-based care management intensity on inpatient readmissions in Medicaid adults with pre-existing chronic disease. Retrospective analyses of 2,868 index hospital admissions from 2012 New York State Medicaid Data Warehouse claims compared 90-day post-discharge utilization in populations with and without transitional care management interventions. High intensity managed care organization interventions were associated with higher outpatient and lower emergency department post-discharge utilization than low intensity fee-for-service management. However, readmission rates were higher for the managed care cases. Shorter time to readmission was associated with managed care, diagnoses that include heart and kidney failure, shorter length of stay for index hospitalization, and male sex; with no relationship to age. This unexpected result flags the need to re-evaluate readmission as a quality indicator in the complex Medicaid population. Quality improvement efforts should focus on care continuity during transitions and consider population-specific factors that influence readmission. Optimum post-discharge utilization in the Medicaid population requires a balance between outpatient, emergency and inpatient services to improve access and continuity.

Frequent emergency department utilization and behavioral health diagnoses.

BACKGROUND: There are 12 million emergency department (ED) visits each year related to behavioral health diagnoses. Frequent ED utilization among subpopulations, such as those with behavioral health diagnoses, flags the need for more accessible and effective healthcare delivery systems. Reducing frequent ED use is essential to controlling healthcare cost and poor outcomes of ED overcrowding. OBJECTIVES: The purpose of this study is to stratify individuals by overall health complexity and examine the relationship of behavioral health diagnoses (psychiatric and substance abuse) as well as frequent treat-and-release ED utilization in a cohort of Medicaid recipients. METHODS: This study was a retrospective analysis of 2009 Medicaid claims from two Western New York State counties. The claims represented 56,491 individuals (18-64 years old). Individuals were stratified into four separate cohorts for analysis based on underlying disease complexity. Data were analyzed using logistic regression models. RESULTS: The following factors significantly increased the odds of frequent treat-and-release ED use in all the four complexity cohorts: psychiatric diagnosis (ORs = 1.4-2.3), substance abuse (ORs = 2.4-3.8), and smoking (ORs = 1.7-4.0). Medicaid patients with behavioral health diagnoses show high risk of frequent treat-and-release ED use. DISCUSSION: The results of this study show that psychiatric diagnosis, substance abuse, and smoking are associated with increased odds of frequent treat-and-release ED utilization for Medicaid recipients in all categories of underlying disease complexity. Our findings support associations reported in the literature.

A population-based care transition model for chronically ill elders.

Elders with chronic illness are hospitalized more often than those without major chronic disease, and nearly one-fifth of hospitalizations result in re-admission within 30 days of discharge from the hospital. Care transition management programs address chronic disease complexity to reduce unnecessary hospitalization, improve quality of care, and reduce medical expense. This report describes how informatics influenced the transformation of a regional managed care organization from one focused on specific chronic disease prevalence to one targeting population-specific chronic conditions based on complexity. The key implication of these results is that population-based informatics can amplify the impact of programs designed to improve quality and prevent avoidable admissions and, at the same time, speed the rate of translation of evidence-based interventions to entire populations. This approach demonstrated an effective, efficient way to translate evidence-based research to the Medicare population, smoothing the transition back into the community, and preventing avoidable hospital admissions.