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.

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.

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.

CIC 2022: 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

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=3438) 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.

CONCLUSIONS

This manuscript provides a practical method for analysis of primary care practice data using machine learning in tandem with expert clinical knowledge.   Keywords: Social determinants of health; phenotypes; primary care; nursing; ambulatory care information systems; machine learning; care coordination; provider- provider communication; knowledge translation.

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.

Transitional Care Models for High-Need, High-Cost Adults in the United States: A Scoping Review and Gap Analysis

Purpose of Study:

This scoping review explored research literature on the integration and coordination of services for high-need, high-cost (HNHC) patients in an attempt to answer the following questions: What models of transitional care are utilized to manage HNHC patients in the United States ? and How effective are they in reducing low-value utilization and in improving continuity ?

Primary Practice Settings:

U.S. urban, suburban, and rural health care sites within primary care, veterans’ services, behavioral health, and palliative care.

Methodology and Sample:

Utilizing the Joanna Briggs Institute and PRISMA guidelines for scoping reviews, a stepwise method was applied to search multiple databases for peer-reviewed published research on transitional care models serving HNHC adult patients in the United States from 2008 to 2018. All eligible studies were included regardless of quality rating. Exclusions were foreign models, studies published prior to 2008, review articles, care reports, and studies with participants younger than 18 years. The search returned 1,088 studies, of which 19 were included.

Results:

Four studies were randomized controlled trials and other designs included case reports and observational, quasi-experimental, cohort, and descriptive studies. Studies focused on Medicaid, Medicare, dual-eligible patients, veterans, and the uninsured or underinsured. High-need, high-cost patients were identified on the basis of prior utilization patterns of inpatient and emergency department visits, high cost, multiple chronic medical diagnoses, or a combination of these factors. Tools used to identify these patients included the hierarchical condition category predictive model, the Elder Risk Assessment, and the 4-year prognostic index score. The majority of studies combined characteristics of multiple case management models with varying levels of impact.

Implications for Case Management Practice:

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.

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.

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.

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.

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.

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.

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.

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.

Aligning population-based care management with chronic disease complexity

BACKGROUND

Risk-stratified care management requires knowledge of the complexity of chronic disease and comorbidity, information that is often not readily available in the primary care setting. The purpose of this article was to describe a population-based approach to risk-stratified care management that could be applied in primary care.

METHODS

Three populations (Medicaid, Medicare, and privately insured) at a regional health plan were divided into risk-stratified cohorts based on chronic disease and complexity, and utilization was compared before and after the implementation of population-specific care management teams of nurses.

RESULTS

Risk-stratified care management was associated with reductions in hospitalization rates in all three populations, but the opportunities to avoid admissions were different.

CONCLUSIONS

Knowledge of population complexity is critical to the development of risk-stratified care management in primary care, and a complexity matrix can help nurses identify gaps in care and align interventions to cohort and population needs.

Integrating pay for performance with educational strategies to improve diabetes care

Improving quality at the point of care, the practice site, has become the focal point of many health quality initiatives. Practice sites vary greatly in their levels of knowledge, comfort, and willingness to embark upon quality improvement activities. The objective of this study was to improve consistency of adherence to diabetes evidence-based guidelines; to engage physicians in critical review of their practice patterns around care of diabetic patients; and to change office systems to improve care. The survey used the Diabetes Chart Review Tool for 9 quality improvement cycles at 6-month intervals. The participants were adult primary care physicians with a minimum of 170 commercial members (n = 170-331 physicians, depending on cycle). Participating physicians received a random sample of 15 patients with diabetes for whom they review their medical records to complete a diabetes questionnaire. The survey was scored and physician-specific detail and summary reports were generated. Reports were reviewed with physicians by health plan representatives. The survey was monetary incentive participation-based in early cycles, with performance-based incentives added after the third year. The main outcome measure was the rate of cases meeting specific diabetes process and outcome measures and composite adherence to guideline score. The results were a participation rate above 84% for eligible physicians (32,069 chart reviews), and steady improvement in all process and outcome measures. Adherence to diabetes clinical guidelines shows statistically significant improvement (Student's t test, P < 0.001, mean difference -1.8, confidence interval 1.9-1.7) from baseline. The program achieved significant improvement in comprehensive diabetes care at the physician practice site level. Success is attributed to engagement of physicians, actionable reports, office-based education, written action plans, and alignment with our internal disease management.