Redesigning Diabetes Care for Treatment Inertia: A Population Health Model.

In the past 2 decades, health care has witnessed technological and pharmacological advancements leading to innovations in diabetes management. Despite these advances, published guidelines, and treatment algorithms, most people with diabetes remain above glycemic targets. Thus, the authors designed a novel care model aimed at improving several causative factors, including therapeutic inertia, limited access to endocrinology and cardiovascular specialists, time constraints, and complexity in incorporating clinical practice guidelines. The model involves collaboration between the diabetes specialty team and primary care providers (PCPs). The intervention reviewed uncontrolled diabetes data and the patient's electronic medical record (EMR) and sent personalized, evidence-based recommendations to the provider using the task function in the EMR. Other services (eg, diabetes education) were utilized to optimize patient care to achieve optimal glycemic targets and address cardiometabolic risk. The overall mean hemoglobin A1c (HbA1c) decreased pre-post intervention by almost 1%, and 52.1% (347 of 666) of the cohort had ≥-0.5% change in HbA1c post-intervention. All pathways exhibited a decrease in HbA1c. Team-based approaches to managing diabetes patient care were the most effective. The interventions effectively utilized the resources across the health system without placing additional load or burden on primary care or diabetes specialty care teams. In the future, the authors hope to address the limitations of the current gap caused by increasing diabetes numbers, decreasing availability of PCPs and endocrinologists, and fee-for-service models using the innovative specialty consultant-primary care connection and knowledge exchange offered by this novel model, which can only be sustained with payer's support.

Predicting pressure injury risk in hospitalised patients using machine learning with electronic health records: a US multilevel cohort study.

OBJECTIVE: To predict the risk of hospital-acquired pressure injury using machine learning compared with standard care. DESIGN: We obtained electronic health records (EHRs) to structure a multilevel cohort of hospitalised patients at risk for pressure injury and then calibrate a machine learning model to predict future pressure injury risk. Optimisation methods combined with multilevel logistic regression were used to develop a predictive algorithm of patient-specific shifts in risk over time. Machine learning methods were tested, including random forests, to identify predictive features for the algorithm. We reported the results of the regression approach as well as the area under the receiver operating characteristics (ROC) curve for predictive models. SETTING: Hospitalised inpatients. PARTICIPANTS: EHRs of 35 001 hospitalisations over 5 years across 2 academic hospitals. MAIN OUTCOME MEASURE: Longitudinal shifts in pressure injury risk. RESULTS: The predictive algorithm with features generated by machine learning achieved significantly improved prediction of pressure injury risk (p<0.001) with an area under the ROC curve of 0.72; whereas standard care only achieved an area under the ROC curve of 0.52. At a specificity of 0.50, the predictive algorithm achieved a sensitivity of 0.75. CONCLUSIONS: These data could help hospitals conserve resources within a critical period of patient vulnerability of hospital-acquired pressure injury which is not reimbursed by US Medicare; thus, conserving between 30 000 and 90 000 labour-hours per year in an average 500-bed hospital. Hospitals can use this predictive algorithm to initiate a quality improvement programme for pressure injury prevention and further customise the algorithm to patient-specific variation by facility.

Race, Ethnicity, and Intensive Care Utilization for Common Pediatric Diagnoses: U.S. Pediatric Health Information System 2019 Database Study.

OBJECTIVES: Racial and ethnic disparities in healthcare delivery for acutely ill children are pervasive in the United States; it is unknown whether differential critical care utilization exists. DESIGN: Retrospective study of the Pediatric Health Information System (PHIS) database. SETTING: Multicenter database of academic children's hospitals in the United States. PATIENTS: Children discharged from a PHIS hospital in 2019 with one of the top ten medical conditions where PICU utilization was present in greater than or equal to 5% of hospitalizations. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Race and ethnicity categories included Asian, Black, Hispanic, White, and other. Primary outcomes of interest were differences in rate of PICU admission, and for children requiring PICU care, total hospital length of stay (LOS). One-quarter (n = 44,200) of the 178,134 hospital discharges included a PICU admission. In adjusted models, Black children had greater adjusted odds ratio (aOR [95% CI]) of PICU admission in bronchiolitis (aOR, 1.08 [95% CI, 1.02-1.14]; p = 0.01), respiratory failure (aOR, 1.18 [95% CI, 1.10-1.28]; p < 0.001), seizure (aOR, 1.28 [95% CI, 1.08-1.51]; p = 0.004), and diabetic ketoacidosis (DKA) (aOR, 1.18 [95% CI, 1.05-1.32]; p = 0.006). Together, Hispanic, Asian, and other race children had greater aOR of PICU admission in five of the diagnostic categories, compared with White children. The geometric mean (± sd) hospital LOS ranged from 47.7 hours (± 2.1 hr) in croup to 206.6 hours (± 2.8 hr) in sepsis. After adjusting for demographics and illness severity, non-White children had longer LOS in respiratory failure, pneumonia, DKA, and sepsis. CONCLUSIONS: The need for critical care to treat acute illness in children may be inequitable. Additional studies are needed to understand and eradicate differences in PICU utilization based on race and ethnicity.

Building for Value: A Foundational Structure to Support Population Health.

The journey to value relies heavily on a strong foundation in population health and on supporting systems of care. However, as the Centers for Medicare & Medicaid Services and commercial insurers rethink reimbursements to achieve cost savings, both patients and payments to health care organizations are at risk. The case for value-based care is ever stronger yet health systems will have to mature their culture, population health infrastructure, technologies and analytics capabilities, and leadership and management systems. In this article, the authors describe the functional organizational structure of the clinical transformation team responsible for population health in the University Hospitals Accountable Care Organizations (ACO). Based on their experiences building and evolving population health for the University Hospitals ACO, the authors layout the 3 pillars supporting their structure, including operations, clinical design, and data and analytics, and key areas of focus for each pillar.

A Path to Risk: Critical Elements of a Structured Approach.

Value-based care arrangements have been the cornerstone of accountable care for decades. Risk arrangements with government and commercial insurance plans are ubiquitous, with most contracts focusing on upside risk only, meaning payers reward providers for good performance without punishing them for poor performance on quality and cost. However, payers are increasingly moving into downside risk arrangements, bringing to mind global capitation in the 1990s wherein several health systems failed. In this article, the authors focus on their framework for succeeding in value-based arrangements at University Hospitals Accountable Care Organization, including essential structural elements that provider organizations need to successfully assume downside risk in value-based arrangements. These elements include quality performance and reporting, risk adjustment, utilization management, care management and clinical services, network integrity, technology, and contracting and financial reconciliation. Each of these elements has an important place in the strategic roadmap to value, even if downside risk is not taken. This roadmap was developed through an applied approach and intends to fill the gap in published practical models of how provider organizations can maneuver value-based arrangements.

Effects of the care given to intensive care patients using an evidence model on the prevention of central line-associated bloodstream infections.

It is important to put evidence-based guidelines into practice in the prevention of central line-associated bloodstream infections in intensive care patients. In contrast to expensive and complex interventions, a care bundle that includes easy-to-implement and low-cost interventions improves clinical outcomes. The compliance of intensive care nurses with guidelines is of great importance in achieving these results. The Translating Evidence into Practice Model provides guidance in how to implement the necessary guidelines. This quasi-experimental study used a post-test control group design in nonequivalent groups and was conducted in the anesthesia intensive care unit of a tertiary-level training and research hospital. All patients who were hospitalized in the intensive care unit, who had a central line during the study, and who met the inclusion criteria were included in the sample. The care bundle comprised education, and protocols for hand hygiene and the aseptic technique, maximum sterile barrier precautions, central line insertion trolley, and management of nursing care. To analyze the data, the independent samples t-test, the Mann-Whitney U test, chi-square test, dependent samples t-test, rate ratio, and relative risk were used with 95% confidence intervals. The rate of central line-associated bloodstream infections was significantly lower in the intervention group (2.85/1000 central line days) than in the control group (3.35/1000 central line days) (P = 0.042). The number of accesses to the central line by the nurses decreased significantly in the intervention group compared to the control group (P < 0.001). The mean score for the nurses' evidence-based guideline post-education knowledge (70.80 ± 12.26) was significantly higher than that pre-education (48.20 ± 14.66) (P < 0.001). Compliance with the guideline recommendations in central line-related nursing interventions and in the central line insertion process was significantly better in the intervention group than in the control group in many interventions (P < 0.05). The mean score for the nurses' attitude towards evidence-based nursing increased significantly over time (59.87 ± 7.23 at the 0th month; 63.79 ± 7.24 at the 6th month) (P < 0.001). Nursing care given by implementing the central line care bundle with the Translating Evidence into Practice Model affected the measures. Thanks to the implementation of the care bundle, the rate of infections and the number of accesses to the central line decreased, while the critical care nurses' knowledge of evidence-based guidelines, compliance with the guideline recommendations in central line-related nursing interventions, and attitudes towards evidence-based nursing improved.

A Conceptual Framework for Building Individual and Team Capabilities to Provide Effective Longitudinal, Relationship-Based Clinical Case Management.

Individuals with complex, chronic diseases represent 5% of the population but consume 50% of the costs of care. These patients have complex lives, characterized by multiple chronic physical health conditions paired with a combination of behavioral health issues and/or unmet social needs. Unlike for most health problems, the problems faced by individuals with complex lives cannot be broken down into simpler parts to be solved independent from 1 another. In this article, the authors describe a 2-phase framework for improving outcomes in patients with complex lives, outline how the model works in more detail, and discuss lessons learned in this journey. In phase 1, a case manager carefully and deliberately focuses on building a relationship with the patient to first gain trust, and then identify, in partnership with the patient, how to best approach assisting the patient in improving their health. That pathway is often unknowable without a deep investment of time, a radical acceptance of the patient, faults and all, and an unwavering commitment to stay by their side, even when things are tough. Once the case manager and patient have established a trusting relationship, they enter phase 2-building a path toward wellness, including further emphasis on the relationship, solving prioritized issues, changing the health system approach, and engaging the patient in self-reflection and behavior change activities.

Critical Partnerships: How to Develop a Trans-Disciplinary Research Team.

Trans-disciplinary science will continue to be critical for the next wave of scientific advancement to fully understand cancer development, progression, and treatment. The shift from the independent investigator to either leading or being a productive member of a scientific team can be successful by focusing on some key elements that can build and strengthen interactions with a diverse group of people. These include the selection of the team, communication, leadership and mentorship, shared goals, responsibility to the team, authorship, and proactively dealing with conflict. While there are extensive books written on developing teams in the business world, and larger pieces in the medical arena, we attempt to provide here a concise, high-level view as a starting point for those that may be moving from being an independent researcher and are developing their own, larger, trans-disciplinary teams.

Community-Based Hospitals Benefit From Restrictive Transfusion Practices.

ABSTRACT: Blood availability was uncertain during the COVID-19 pandemic, yet transfusion remained a common and sometimes necessary procedure. Substantial work on optimizing transfusion practices is centered in tertiary hospitals as high utilizers of blood while the care delivered in smaller community hospitals comprises more than half the nation's transfusions. Improving transfusion practices in community hospitals represents a substantial opportunity to enhance patient safety and the availability of blood resources. Clinical specialists developed a dashboard to retrospectively examine transfusion events including an evidence-based analysis of the patient's clinical situation at the time of transfusion to more accurately identify how appropriately blood was used. The compiled data were discussed and shared with transfusing providers. It was hypothesized that the data provided and communication strategies used would educate providers to current evidence-based practice, leading to more appropriate transfusion with an overall reduction in packed red blood cell utilization. There was an 11% increase in transfusion appropriateness (p = <.001) and a 14% decrease in the units transfused (p = .004). Improvement in transfusion practices demonstrates a significant impact on patient safety and the availability of blood resources. Although absolute opportunity may be less in a community hospital, fewer resources are needed to achieve meaningful change.

Misdiagnosis in the Emergency Department: Time for a System Solution.

This Viewpoint offers 3 insights in response to the AHRQ report on diagnostic errors made in US emergency departments: focus on the delivery systems instead of individuals, establish ways to set definitions and assess error rates, and design safe delivery systems to prevent errors.